{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic data analysis or exploratory data analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read Training dataset as well as drop the index column\n",
    "training_data = pd.read_csv('./data/cs-training.csv').drop('Unnamed: 0', axis = 1)\n",
    "\n",
    "\n",
    "# For each column heading we replace \"-\" and convert the heading in lowercase \n",
    "cleancolumn = []\n",
    "for i in range(len(training_data.columns)):\n",
    "    cleancolumn.append(training_data.columns[i].replace('-', '').lower())\n",
    "training_data.columns = cleancolumn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seriousdlqin2yrs</th>\n",
       "      <th>revolvingutilizationofunsecuredlines</th>\n",
       "      <th>age</th>\n",
       "      <th>numberoftime3059dayspastduenotworse</th>\n",
       "      <th>debtratio</th>\n",
       "      <th>monthlyincome</th>\n",
       "      <th>numberofopencreditlinesandloans</th>\n",
       "      <th>numberoftimes90dayslate</th>\n",
       "      <th>numberrealestateloansorlines</th>\n",
       "      <th>numberoftime6089dayspastduenotworse</th>\n",
       "      <th>numberofdependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seriousdlqin2yrs  revolvingutilizationofunsecuredlines  age  \\\n",
       "0                 1                              0.766127   45   \n",
       "1                 0                              0.957151   40   \n",
       "2                 0                              0.658180   38   \n",
       "3                 0                              0.233810   30   \n",
       "4                 0                              0.907239   49   \n",
       "\n",
       "   numberoftime3059dayspastduenotworse  debtratio  monthlyincome  \\\n",
       "0                                    2   0.802982         9120.0   \n",
       "1                                    0   0.121876         2600.0   \n",
       "2                                    1   0.085113         3042.0   \n",
       "3                                    0   0.036050         3300.0   \n",
       "4                                    1   0.024926        63588.0   \n",
       "\n",
       "   numberofopencreditlinesandloans  numberoftimes90dayslate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "3                                5                        0   \n",
       "4                                7                        0   \n",
       "\n",
       "   numberrealestateloansorlines  numberoftime6089dayspastduenotworse  \\\n",
       "0                             6                                    0   \n",
       "1                             0                                    0   \n",
       "2                             0                                    0   \n",
       "3                             0                                    0   \n",
       "4                             1                                    0   \n",
       "\n",
       "   numberofdependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the 5 records of the traiing dataset\n",
    "training_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revolvingutilizationofunsecuredlines</th>\n",
       "      <th>age</th>\n",
       "      <th>numberoftime3059dayspastduenotworse</th>\n",
       "      <th>debtratio</th>\n",
       "      <th>monthlyincome</th>\n",
       "      <th>numberofopencreditlinesandloans</th>\n",
       "      <th>numberoftimes90dayslate</th>\n",
       "      <th>numberrealestateloansorlines</th>\n",
       "      <th>numberoftime6089dayspastduenotworse</th>\n",
       "      <th>numberofdependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.202690e+05</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>146076.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.048438</td>\n",
       "      <td>52.295207</td>\n",
       "      <td>0.421033</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>6.670221e+03</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>0.265973</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>0.757222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>249.755371</td>\n",
       "      <td>14.771866</td>\n",
       "      <td>4.192781</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>1.438467e+04</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>4.169304</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>4.155179</td>\n",
       "      <td>1.115086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.400000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.154181</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.559046</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>8.249000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       revolvingutilizationofunsecuredlines            age  \\\n",
       "count                         150000.000000  150000.000000   \n",
       "mean                               6.048438      52.295207   \n",
       "std                              249.755371      14.771866   \n",
       "min                                0.000000       0.000000   \n",
       "25%                                0.029867      41.000000   \n",
       "50%                                0.154181      52.000000   \n",
       "75%                                0.559046      63.000000   \n",
       "max                            50708.000000     109.000000   \n",
       "\n",
       "       numberoftime3059dayspastduenotworse      debtratio  monthlyincome  \\\n",
       "count                        150000.000000  150000.000000   1.202690e+05   \n",
       "mean                              0.421033     353.005076   6.670221e+03   \n",
       "std                               4.192781    2037.818523   1.438467e+04   \n",
       "min                               0.000000       0.000000   0.000000e+00   \n",
       "25%                               0.000000       0.175074   3.400000e+03   \n",
       "50%                               0.000000       0.366508   5.400000e+03   \n",
       "75%                               0.000000       0.868254   8.249000e+03   \n",
       "max                              98.000000  329664.000000   3.008750e+06   \n",
       "\n",
       "       numberofopencreditlinesandloans  numberoftimes90dayslate  \\\n",
       "count                    150000.000000            150000.000000   \n",
       "mean                          8.452760                 0.265973   \n",
       "std                           5.145951                 4.169304   \n",
       "min                           0.000000                 0.000000   \n",
       "25%                           5.000000                 0.000000   \n",
       "50%                           8.000000                 0.000000   \n",
       "75%                          11.000000                 0.000000   \n",
       "max                          58.000000                98.000000   \n",
       "\n",
       "       numberrealestateloansorlines  numberoftime6089dayspastduenotworse  \\\n",
       "count                 150000.000000                        150000.000000   \n",
       "mean                       1.018240                             0.240387   \n",
       "std                        1.129771                             4.155179   \n",
       "min                        0.000000                             0.000000   \n",
       "25%                        0.000000                             0.000000   \n",
       "50%                        1.000000                             0.000000   \n",
       "75%                        2.000000                             0.000000   \n",
       "max                       54.000000                            98.000000   \n",
       "\n",
       "       numberofdependents  \n",
       "count       146076.000000  \n",
       "mean             0.757222  \n",
       "std              1.115086  \n",
       "min              0.000000  \n",
       "25%              0.000000  \n",
       "50%              0.000000  \n",
       "75%              1.000000  \n",
       "max             20.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the all statistical properties of the training dataset\n",
    "training_data[training_data.columns[1:]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "revolvingutilizationofunsecuredlines       0.154181\n",
       "age                                       52.000000\n",
       "numberoftime3059dayspastduenotworse        0.000000\n",
       "debtratio                                  0.366508\n",
       "monthlyincome                           5400.000000\n",
       "numberofopencreditlinesandloans            8.000000\n",
       "numberoftimes90dayslate                    0.000000\n",
       "numberrealestateloansorlines               1.000000\n",
       "numberoftime6089dayspastduenotworse        0.000000\n",
       "numberofdependents                         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data[training_data.columns[1:]].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "revolvingutilizationofunsecuredlines       6.048438\n",
       "age                                       52.295207\n",
       "numberoftime3059dayspastduenotworse        0.421033\n",
       "debtratio                                353.005076\n",
       "monthlyincome                           6670.221237\n",
       "numberofopencreditlinesandloans            8.452760\n",
       "numberoftimes90dayslate                    0.265973\n",
       "numberrealestateloansorlines               1.018240\n",
       "numberoftime6089dayspastduenotworse        0.240387\n",
       "numberofdependents                         0.757222\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data[training_data.columns[1:]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    93.316\n",
       "1     6.684\n",
       "Name: seriousdlqin2yrs, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This give you the calulation of the target lebels. Which category of the target lebel is how many percentage.\n",
    "total_len = len(training_data['seriousdlqin2yrs'])\n",
    "percentage_labels = (training_data['seriousdlqin2yrs'].value_counts()/total_len)*100\n",
    "percentage_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vyZqEEELUAxY/B9OtWzc+/fRTUlNTAfDx8WHIkCHodDqrFyeEEKLushgwS5cu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0cbcd3de80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphical representation of the target label percentage.\n",
    "sns.set()\n",
    "sns.countplot(training_data.seriousdlqin2yrs).set_title('Data Distribution')\n",
    "ax = plt.gca()\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x() + p.get_width()/2.,\n",
    "            height + 2,\n",
    "            '{:.2f}%'.format(100*(height/total_len)),\n",
    "            fontsize=12, ha='center', va='bottom')\n",
    "sns.set(font_scale=1.5)\n",
    "ax.set_xlabel(\"Labels for seriousdlqin2yrs attribute\")\n",
    "ax.set_ylabel(\"Numbers of records\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seriousdlqin2yrs                            0\n",
       "revolvingutilizationofunsecuredlines        0\n",
       "age                                         0\n",
       "numberoftime3059dayspastduenotworse         0\n",
       "debtratio                                   0\n",
       "monthlyincome                           29731\n",
       "numberofopencreditlinesandloans             0\n",
       "numberoftimes90dayslate                     0\n",
       "numberrealestateloansorlines                0\n",
       "numberoftime6089dayspastduenotworse         0\n",
       "numberofdependents                       3924\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You will get to know which column has missing value and it's give the count that how many records are missing \n",
    "training_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fM2zYMIYNG3bP99zrfTVq1GDdunXFykcIIUTJM1lIunXrRlJSEkOHDqVPnz7k5eUxduxY\nPXITQghhAUwWkoKbBj08PPjjjz/IzMzE3l77QUQhhBCWwWQhuVOpUqUoVaqUVrkIIYSwQCbvIxFC\nCCHuRwqJEEKIRyKFRAghxCMxOUZy8eJFvvzyS2JiYgot0b5+/XpNExNCCGEZTBaSiRMn0qVLFwID\nA9V7SoQQQogCJgtJXl6euhaWEEII8U8mx0iaNWtGVFSUHrkIIYSwQEX2SHr27InBYCAnJ4eNGzdS\nu3ZtypQpoz4vYyRCCCHgPoVk6tSpeuYhhBDCQhVZSFq2bKlnHkIIISyUyTGSPn36kJycrD6+efMm\nffv21TQpIYQQlsNkIUlPT6d8+fLq42eeeYa0tDRNkxJCCGE5TBaSvLw8MjIy1MdpaWmFbkwUQgjx\ndDN5H0n37t0ZOHAgffr0AeD777+nR48emicmhBDCMpgsJMOGDcPZ2Zlff/0VgNdffx1/f3/NExNC\nCGEZirUfSUBAAAEBAVrnIoQQwgKZLCQ5OTls2LCBM2fOkJmZqbYvWLBA08SEEEJYBpOD7bNmzeLo\n0aPs2bOHZ599llOnTlG2bFk9chNCCGEBTBaSkydPsmjRIhwcHBg2bBj//e9/OX/+vB65CSGEsAAm\nC0nB+lrW1tZkZGTg4ODAjRs3NE9MCCGEZTA5RlK+fHmSk5Np3749Q4YMoUKFCri4uOiRmxBCCAtg\nspCsWrUKa2trJkyYwObNm0lJSZHpv0IIIVQmC4m1tTWpqan8/fff+Pn56ZGTEEIIC2JyjGTv3r34\n+PgwZswYIH/wXXZMFEIIUcBkIfnss89Yv349jo6OADRp0oTo6GjNExNCCGEZTBYSACcnp0KPS5cu\nrUkyQgghLI/JQmJnZ8f169cxGAwARERE4ODgUKwPj4+P56233sLHxwdfX1++/fZbAJKTkxk0aBDe\n3t4MHjyYlJQU9T3z5s2jc+fO+Pn5cebMGbU9JCQEb29vvL29CQ0NVdtPnz6Nr68v3t7ezJ8/v3hH\nLYQQosSYLCSTJ09myJAhxMbG0q9fPyZPnlzsbXitra2ZNm0aW7Zs4YcffmDt2rVcuHCBVatW0aZN\nG3bs2EGrVq1YuXIlkD8eEx0dzc6dO5kzZw7BwcFAfuH5/PPPWb9+PevWrWP58uVq8Zk9ezbz589n\nx44dXL58md9+++1hfxZCCCEegslZW+7u7nz77bccPXoUgObNm6vjJaY4OTmpl8Xs7Oxwc3PDaDQS\nHh7Of/7zHyB/Qci33nqLyZMnEx4erk4tbtq0KSkpKVy/fp2IiAjatm2r9oTatm3Lb7/9xksvvURa\nWhru7u4A+Pv788svv9C+ffsH/DEIIYR4WMVa/dfBwYEOHTo8UqDY2FiioqJo2rQpN27coHLlykB+\nsSm4U/7atWtUqVJFfU+VKlUwGo0YjUaqVq2qtru4uKjtd76+oF0IIYR+ilVIHlVaWhpjx45l+vTp\n2NnZqeMtBf75uICiKJrkU6FCOWxsrDX5bCEKJCXZax6jYkV7nJyKN2YphFY0LyQ5OTmMHTsWPz8/\nvLy8AKhUqRLXr1+ncuXKJCQkULFiRQCcnZ2Jj49X3xsfH4+LiwsuLi5EREQUam/dujUuLi5cvXpV\nbTcajcVaviUpKb2kDk+IIiUmpuoSIyEhxfQLhSgBRX1pue9ge25uLj/++OMjBZ4+fTp169alf//+\napunpycbN24E8mdjderUCYBOnTqpM7KOHTuGo6MjlStXpl27dhw4cICUlBSSk5M5cOAA7dq1w8nJ\nCQcHB06cOIGiKISGhqqfJYQQQh/37ZFYW1vz448/8tprrz3Uhx85coTNmzdTv359/P39MRgMTJgw\ngSFDhjB+/Hg2bNiAq6srS5cuBaBDhw7s3buXV199FVtbW3XzrPLlyzNy5Eh69uyJwWBg9OjR6oD/\nrFmzmDZtGpmZmXh4eODh4fFQuQohhHg4BsXEQMSiRYto2rQpXbp00SsnzcmlAKGHCxfOMfVQGPbV\ntFktO/WKkUUvdcfNrZ4mny/EPxV1acvkGElISAhr1qyhbNmy2NraoigKBoOBgwcPlniSQgghLI/J\nQrJhwwY98hBCCGGhTBYSV1dXcnJyuHTpEgC1a9fGxkaXWcNCCCEsgMmKcPLkScaOHUvp0qVRFIWc\nnByWLVvG888/r0d+QgghHnMmC8n8+fP54IMPaNOmDQAHDx5k7ty5/PDDD5onJ4QQ4vFnctHGjIwM\ntYgAtGnThoyMDE2TEkIIYTlMFhJbW9tCd5X/8ccf2NraapqUEEIIy2Hy0tb06dMZN26cuplVdnY2\nn332meaJCSGEsAxFFpI//viDli1b0rBhQ3bu3Flo1lapUqV0S1AIIcTjrchLWwsXLgTgtddeo1Sp\nUtSvX5/69etLERFCCFFIkT2S7Oxs/vWvf5GYmMjatWvver5v376aJiaEEMIyFFlI5syZw6ZNm7h9\n+zanTp3SMychhBAWpMhC0rx5c5o3b06NGjUYPHiwnjkJIYSwICan/0oREUIIcT8mC4kQQghxP1JI\nhBBCPBIpJEIIIR6JyUKycOFCUlJSyMnJ4Y033qBZs2Zs2rRJj9yEEEJYAJOF5MCBAzg4OLB//35c\nXFzYsWMH//rXv/TITQghhAUo9qWtQ4cO8eqrr+Li4oLBYNAyJyGEEBbEZCGpVKkSwcHBbNu2jbZt\n25KTk0Nubq4euQkhhLAAJgvJxx9/TO3atVmyZAnly5cnPj6egQMH6pGbEEIIC2ByGfmKFSsyYMAA\n9XH16tWpXr26ljkJIYSwIEUWktatW993LOTgwYOaJCSEEMKyFFlINmzYAMD69eu5efMmr732Goqi\nsH79esqXL69bgkIIIR5vRRYSV1dXAPbu3cvGjRvV9pkzZ9KzZ0/Gjh2rfXZCCCEeeyYH21NTU0lM\nTFQfJyYmkpqaqmlSQgghLIfJwfb+/fvj7+9Px44dgfweyrBhw7TOSwghhIUw2SPp27cvq1evpl69\netSrV49Vq1bxxhtvFOvDp0+fzssvv4yvr6/atnz5cjw8PAgICCAgIIB9+/apz61cuZLOnTvTtWtX\n9u/fr7bv27ePLl264O3tzapVq9T22NhYevfujbe3NxMnTiQnJ6dYeQkhhCg59y0kubm5DBs2jAYN\nGtCvXz/69etHgwYNiv3hgYGBfP3113e1Dxw4kJCQEEJCQvDw8ADgwoULbNu2ja1bt7J69Wref/99\nFEUhLy+PuXPn8vXXXxMWFsaWLVu4cOECAB999BEDBw5kx44dODg4sH79+gc5diGEECXgvoXE2tqa\nmzdvkpeX91Af3qJFCxwdHe9qVxTlrrbw8HC6deuGjY0N1atXp1atWpw4cYITJ05Qq1YtXF1dKVWq\nFD4+PoSHhwPw+++/4+3tDUBAQAC7du16qDyFEEI8PJNjJE2bNmX06NF0794dOzs7tb1Dhw4PHXTt\n2rVs2rSJxo0b8+677+Lg4IDRaKRZs2bqa1xcXDAajSiKQtWqVQu1nzx5kqSkJMqXL4+VVX4trFKl\nCteuXXvonIQQQjwck4XkzJkzAHz//fdqm8FgeOhC8sYbbzBq1CgMBgOffPIJCxcuZP78+Q/1Wffq\n2RRHhQrlsLGxfqj3ClFcSUn2mseoWNEeJycHzeMIcT8mC8l3331XogErVqyo/r13794MHz4cyO9p\nXL16VX0uPj4eFxcXFEXhypUrarvRaMTZ2ZkKFSpw69Yt8vLysLKyUl9fHElJ6SV0NEIULTFR+2ny\niYmpJCSkaB5HCKDILy3FWkb+t99+Y9GiRSxatIj//e9/DxT4n72GhIQE9e+7du2ifv36AHh6erJ1\n61aysrKIiYkhOjoad3d3mjRpQnR0NHFxcWRlZbFlyxY6deoE5C/jsn37dgBCQkLUdiGEEPox2SP5\n6quvCA0NxcfHB8jfMdHf35/Bgweb/PBJkyYRERHBzZs36dixI2PGjCEiIoIzZ85gZWWFq6src+bM\nAaBu3bp07doVHx8fbGxsCA4OxmAwYG1tzcyZMxk0aBCKohAUFISbm5v6+RMnTuTTTz+lUaNGBAUF\nPcrPQgghxEMwKCYGGnx9ffn++++xt8+/3puamkqfPn3YvHmzLglqQS4FCD1cuHCOqYfCsK9WvEuu\nDyr1ipFFL3XHza2eJp8vxD890qWtgiLyz78LIYQQJi9tNW7cmGnTptGrVy8gfzXgxo0ba56YEEII\ny2CykMycOZMvvviCefPmAfDyyy8zcuRIzRMTQghhGUwWknLlyjF58mQ9chFCCGGBTI6RzJs3j5s3\nb6qPk5KSHvoGQiGEEE8ek4Xk8OHDPPPMM+rjChUqcOjQIU2TEkIIYTlMFpLc3Ny72mS5diGEEAVM\nFpImTZowb948jEYj8fHxzJs3jyZNmuiRmxBCCAtgspBMnz6dtLQ0/P39CQwMJD09nenTp+uRmxBC\nCAtgctaWvb09CxYs0CMXIYQQFshkjyQjI4OlS5cyadIkIH8nw19++UXzxIQQQlgGk4Vk9uzZ5OTk\nEBUVBeRvILV8+XLNExNCCGEZTBaSs2fPMnnyZEqVKgWAnZ3dQ2+9K4QQ4sljspCULl260OPMzMyH\n3plQCCHEk8fkYHuLFi1YsWIFWVlZREREsGbNGjw9PfXITQghhAUw2SOZMGECiqJgZ2fH4sWLcXd3\nZ8yYMXrkJoQQwgLct0eSm5vLxo0bGTFiBCNGjNArJyGEEBbkvj0Sa2trfvzxR71yEUIIYYFMXtpq\n1aoV27dv1yMXIYQQFsjkYHtISAhr1qyhbNmy2NraoigKBoOBgwcP6pGfEEKIx5zJQrJhwwY98hBC\nCGGhTBYSV1dXPfIQQghhoUyOkQghhBD3I4VECCHEI5FCIoQQ4pGYHCNZu3btXW0ODg64u7vz7LPP\napGTEEIIC2KykPz2228cOnSINm3aAPD777/TtGlTlixZwujRowkKCtI8SSGEEI8vk4XEYDCwefNm\nqlWrBsDVq1d5//33WbduHQMHDpRCIoQQTzmTYySxsbFqEQGoWrUqcXFxODk5YW1tfd/3Tp8+nZdf\nfhlfX1+1LTk5mUGDBuHt7c3gwYNJSUlRn5s3bx6dO3fGz8+PM2fOqO0hISF4e3vj7e1NaGio2n76\n9Gl8fX3x9vZm/vz5xTtiIYQQJcpkIalUqRIrVqzg2rVrXLt2jZUrV1KxYkVyc3MxGAz3fW9gYCBf\nf/11obZVq1bRpk0bduzYQatWrVi5ciUAe/fuJTo6mp07dzJnzhyCg4OB/MLz+eefs379etatW8fy\n5cvV4jN79mzmz5/Pjh07uHz5Mr/99ttD/RCEEEI8PJOFZNGiRfz555/4+vri6+vL6dOnWbRoETk5\nOSxatOi+723RogWOjo6F2sLDwwkICAAgICCA8PBwtd3f3x+Apk2bkpKSwvXr19m/fz9t27bFwcEB\nR0dH2rZty2+//UZCQgJpaWm4u7sD4O/vL3vJCyGEGZgcI3FxceGzzz6753MNGjR44ICJiYlUrlwZ\nACcnJ27cuAHAtWvXqFKlivq6KlWqYDQaMRqNVK1atVA+Be13vr6gXQghhL5MFhKAgwcPEh0dTU5O\njtrWt2/fEkmgqMtjWm7nW6FCOWxs7j++I8SjSkqy1zxGxYr2ODk5aB5HiPsxWUjeffddTp06xXPP\nPWdycL04KlWqxPXr16lcuTIJCQlUrFgRAGdnZ+Lj49XXxcfH4+LigouLCxEREYXaW7dujYuLC1ev\nXlXbjUYjLi4uxcohKSn9kY9DCFMSE1N1iZGQkGL6hUKUgKK+tJgsJJGRkYSFhVGqVKmHCvzPnoWn\npycbN25k6NChhISE0KlTJwA6derE2rVr6datG8eOHcPR0ZHKlSvTrl07PvnkE1JSUsjLy+PAgQNM\nnjwZR0dHHBwcOHHiBE2aNCE0NJR+/fo9VI5CCCEenslCcuc4xIOaNGkSERER3Lx5k44dOzJmzBiG\nDh3KuHHj2LBhA66urixduhSADh06sHfvXl599VVsbW1ZsGABAOXLl2fkyJH07NkTg8HA6NGj1QH8\nWbNmMW3aNDIzM/Hw8MDDw+OhcxVCCPFwDIqJwYjg4GDOnz+Pl5cXpUuXVttLaozEHORSgNDDhQvn\nmHooDPsQs8S6AAAgAElEQVRqxbvk+qBSrxhZ9FJ33NzqafL5QvzTQ1/aysrKombNmvz1118lnpQQ\nQgjLZ7KQFFxiEkIIIe6lyEJy5MgRXnzxRfbu3XvP5zt06KBZUkIIISxHkYUkJCSEF198ka+++uqu\n5wwGgxQSIYQQwH0Kybx58wD47rvvdEtGCCGE5TG51tahQ4dIS0sDYN26dcyaNYuYmBjNExNCCGEZ\nTBaSOXPmUK5cOc6dO8eaNWuoVq0aM2bM0CM3IYQQFsBkIbGxscFgMLBv3z769OnD8OHDuXXrlh65\nCSGEsAAmC0lOTg7Hjx9n165dtG7dGoDc3FzNExNCCGEZTBaScePGMWvWLJo1a0a9evW4dOkStWrV\n0iM3IYQQFsDkDYleXl54eXmpj2vXrs3y5cs1TUoIIYTlMNkjWbNmjbq17TvvvEOXLl3Yv3+/5okJ\nIYSwDCYLycaNG3FwcOD3338nMTGRDz74gCVLluiRmxBCCAtgspAUbGYVERGBr68vL7zwgqa7Fwoh\nhLAsJgtJ2bJlWbVqFVu2bKFt27YoikJ2drYeuQkhhLAAJgvJggULSEhIYPLkyTg5ORETE4Ovr68e\nuQkhhLAAJmdt1a5du9Cd7DVr1mTYsGGaJiWEEMJyFFlIFi9ezDvvvMPYsWMxGAx3Pf/pp59qmpgQ\nQgjLUGQhefHFFwF45ZVXdEtGCCGE5SmykHh6egIQEBCgWzJCCCEsj8kxkhs3bvCf//yH6OhocnJy\n1Ha5tCWEEAKKUUhGjhzJc889R5s2bdR7SoQQQogCJgtJRkYGwcHBeuQihBDCApm8j6Rp06acPXtW\nj1yEEEJYIJM9ktdff50333yTKlWqUKZMGbV9/fr1miYmhBDCMpgsJO+88w7Dhw/nueeekzESIYQQ\ndzFZSMqUKcPgwYP1yEUIIYQFMjlG0r59e/bt26dHLkIIISyQyR7JTz/9xKpVq7Czs6N06dIoioLB\nYODgwYOPFNjT0xN7e3usrKywsbFh/fr1JCcnM2HCBOLi4qhevTpLly7FwcEBgHnz5rFv3z5sbW1Z\nuHAhjRo1AiAkJIQVK1YAMGLECPz9/R8pLyGEEA/GZCHZsGGDJoENBgPfffcd5cuXV9tWrVpFmzZt\nGDJkCKtWrWLlypVMnjyZvXv3Eh0dzc6dOzl+/DjBwcH89NNPJCcn8/nnnxMSEoKiKAQGBtKpUye1\n+AghxJNq7tyZHD58iNu3M6hUqTJvvNGP7t3zv0hv3hzK2rX/JjExEXf3prz77iwqV64MwH//+x3b\nt4cRHx/PM888g79/EG+80e+uz4+MPMLYscPp338wb789/L65mLy05erqes8/j0pRFPLy8gq1hYeH\nq0uyBAQEEB4errYX9DSaNm1KSkoK169fZ//+/bRt2xYHBwccHR1p27Ytv/322yPnJoQQj7t+/Qax\nYUMYO3bsZeHCj1m9+kv++iuKo0cPs2rVFyxa9Albt4ZTpUo1Zs+eXui9M2fOYfv23Xz00Wds3PgT\n4eG7Cj2fk5PDZ599zPPPNylWLiYLiVYMBgODBw+mZ8+erFu3DshfjqWgajo5OXHjxg0Arl27RpUq\nVdT3VqlSBaPRiNFopGrVqmq7i4sLRqNRx6MQQgjzePbZ2tjY/N9FJYPBiri4WA4e/B+enl7UqvUs\nNjY2DBjwNsePR3LlShwAb7zRj3r1GmBlZUXNmrVo164DJ08eL/TZP/zwH1q2bEPNmrWKlYvJS1ta\n+f7773F2diYxMZFBgwZRu3btu5arv9fy9cAjb/VboUI5bGxkKrPQVlKSveYxKla0x8lJLuU+rd5/\n/31CQkK4ffs2zz33HL6+Xbh06S+srPLU8yIvLx2AGzeu0LRpw7s+488/T/D666+rr4+Li2PHji2E\nhIQwZ84cypUrbfIcK7KQ/Otf/2LQoEEcOXJEXVK+JDk7OwNQsWJFvLy8OHHiBJUqVeL69etUrlyZ\nhIQEKlasqL42Pj5efW98fDwuLi64uLgQERFRqL1169YmYyclpZfw0Qhxt8TEVF1iJCSkaB5HPJ5G\njpzIiBETOHXqBJGRR0hOzsTdvQWzZ8/A29sXV9fqfPrpJ1hZWXHtWtJd58rXX68kOzsHD4/O6nPB\nwe8zaNAwUlNzuH07m/T0LPW5ogpKkZe2Nm/eDOTPlippGRkZpKWlAZCens7+/fupX78+np6ebNy4\nEcifjdWpUycAOnXqRGhoKADHjh3D0dGRypUr065dOw4cOEBKSgrJyckcOHCAdu3alXi+QgjxuDIY\nDDRp0pRr14yEhq6nRYuWDB48jOnTp9C7tx/Vqrlia1sOJyfnQu/bsOFHduzYyuLFn6mXyPbv30d6\nejqvvOL1QDkU2SMpU6YMw4cPJy4ujnHjxt31/KMsI3/9+nVGjx6NwWAgNzcXX19f2rVrR+PGjRk/\nfjwbNmzA1dWVpUuXAtChQwf27t3Lq6++iq2tLQsWLACgfPnyjBw5kp49e2IwGBg9ejSOjo4PnZcQ\nQliq3Nxc4uJiAQgICCIgIAiAmJho/v3vf1GnTl31tWFhm1i79lu++OIrdVwa4OjRQ5w9ewY/P28A\nUlNTsba24cKF8yxY8FGRsQ1KEQMON2/e5MCBAyxevJixY8fe9bwlb3gllwKEHi5cOMfUQ2HYV3PR\n5PNTrxhZ9FJ33NzqafL54vGVlJTE0aOHePnl9pQpU4ZDhyJ4772pvP/+B7Ro0ZLY2Bjq1HEjPj6e\n+fODcXdvxpAhIwDYuXMbn3++lGXLVlKz5rOFPjcjI4PbtzPUx0uXLqZyZWcGDHgbBweHIi9tFdkj\neeaZZ+jWrRuVKlWiVatWJXDoQgghSoLBYCAkZD0ffbQQRcnDxaUq48ZN4uWX25Gamsr777/HlStx\nlCtXDh+fHoXuA1m9egW3bt3i7bf7qzeYd+7clcmT38XW1hZbW1v1tWXKlMXW1tbkvXlF9kgKKIrC\njz/+yIEDBwBo164dvXr1KnJGlSWQHonQg/RIxJPmgXskBRYvXsyff/5JYGAgAKGhoVy+fJkpU6aU\nbIZCCCGKLTc3l8uXL2oa49ln6xRr1XeTheS3334jJCREHdXv2rUrgYGBUkiEEMKMLl++yOVv9lOz\nYjVNPj868QoMoFg93mLdkHjnZSxLvqQlhBBPkpoVq+HmXNPcaZguJO3atWPIkCHqLK3Q0FC5V0MI\nIYSqWDsk/vjjj+zalb+ol5eXF6+99prmiQkhhLAMJguJlZUVffr0oU+fPnrkI4QQwsKYbfVfIYQQ\nTwYpJEIIIR6JFBIhhBCPxGQhudeCjfdqE0II8XQyWUiio6Pvart4Udu7KYUQQliOImdt/fTTT/z4\n449cvnyZoKAgtT0lJYXatWvrkpwQQojHX5GFpG3bttSqVYu5c+cWWg7F3t6eBg0a6JKcEEKIx1+R\nhcTV1RVXV1fCwsL0zEcIIYSFMXlD4sWLF/nyyy+JiYkhJydHbV+/fr2miQkhhLAMJgvJxIkT6dKl\nC4GBgcVaTlgIIcTTxWQhycvLY/jw4aZeJoQQ4illcvpvs2bNiIqK0iMXIYQQFshkj+TEiRNs3LiR\n2rVrU6ZMGbVdxkiEEEJAMQrJ9OnT9cjjsXfr1i0WLJjD4cMRPPNMBYYOHcmrr3Z5YuOaM/bTeMzm\n9LT9nuX8KnkmC0nLli31yOOx9/HHCyldujRhYbs4ezaKKVPGU69eA559VtubM80V15yxn8ZjNqen\n7fcs51fJMzlG0rNnT4KCgu768zS5ffs2+/btZujQkZQpUxZ392a0bevBjh1bn8i45oz9NB6zOT1t\nv2c5v7RhskcydepU9e+ZmZls2bIFZ2dnTZN63MTE/I2NjQ2urtXVtrp163P8+NEnMq45Yz+Nx2xO\nT9vvWc4vbTzwpa127do9dbslpqdnUK6cXaE2e3t70tPTn8i45oz9NB6zOT1tv2c5v7TxwPuRpKam\ncv36dS1yeWyVK2dLenpaoba0tFTKlSv3RMY1Z+yn8ZjN6Wn7Pcv5pQ2TPZKePXtiMBiA/JsTY2Nj\nGThwoOaJPU5q1KhFbm4ucXGxarf0/Plz1K7t9kTGNWfsp/GYzelp+z3L+aUNkz2SqVOnMmXKFKZM\nmcKMGTMICwtjxIgReuRWbPv27aNLly54e3uzatWqEv/8smXL4uHxCl99tYLbt29z4sQx9u/fh7d3\ntxKP9TjENWfsp/GYzelp+z3L+aUNg6IoiqkX5eTkcOnSJQBq166NjY3Jjoxu8vLy8Pb25ptvvsHZ\n2ZmgoCCWLFmCm1vRVT4hIeWB49w5/7t8+WcYMWIMnTp1fpTUH+u45oz9pBzzhQvnmHooDPtqLiWc\nZb7UK0YWvdQdN7d6j/Q5T9vv+Uk6v/J+voibc80SzvL/f/61aKx61Cl0fjk5OdzztSYLycmTJxk7\ndiylS5dGURRycnJYtmwZzz//fMlm/ZCOHTvG8uXL+eqrrwDUHsnQoUOLfM/DFBIhHpSlFBJhmR6n\nQmKyazF//nw++OAD2rRpA8DBgweZO3cuP/zwQwml+2iMRiNVq1ZVH7u4uHDy5EkzZiSEEE8Xk4Uk\nIyNDLSIAbdq0YeHChZompacLF85p+vlFfVvUOq45Yz9tce8XO+OadjMc7/fZcn49WbGLihudeEWz\nmNGJV3iWOsV6rclCYmtrS0REBK1atQLgjz/+wNbW9tEyLEEuLi5cufJ/P0yj0Wjyhsk7u2dOTi9o\nltv9czBPXHPGfhrj/tH66Tpmc8Z+2o7ZyekFWmt4fr38AK8t1qKN48aNo3Tp0gBkZ2fz2WefPWxu\nJa5JkyZER0cTFxeHk5MTW7ZsYcmSJeZOSwghnhrFmrWVnZ1daNZWqVKlNE/sQezbt4/58+ejKApB\nQUH3HWgXQghRskwWkgMHDtCkSRMcHPIvB926dYvTp08XGjcRQgjx9DJ5Q+KHH36Ivb29+tje3p4P\nP/xQ06SEEEJYDpOFRFEUdYkUACsrK3JzczVNSgghhOUwWUjs7Ow4fvy4+vj48eNPxCJjQgghSobJ\nMZLIyEjGjBlD3bp1ATh//jzLly+nWbNmuiQohBDi8VasWVvJyckcO3YMgGbNmlG+fHnNExNCCGEZ\nilVIxNMpIyPjsbr5VA9pafn7RdjZ2Zl4pbB0ycnJXL16lYYNG5o7Fd1odcwPvLHV0+jDDz8kNTWV\n7Oxs+vfvT+vWrdm0adMTG/fo0aN069aNrl27AhAVFcXs2bM1jwv5kzs2bdrE8uXLAbhy5QonTpzQ\nPO7Zs2fx9/ene/fu+Pj4EBgYyF9//aV5XIBLly7Rv39/unfvDuT/vL/44gvN4x45ckTdnW/Tpk0s\nWLCAuLg4zeOC+Y65X79+pKamcvPmTQICApg5cyYLFizQPC6Y79zW45ilkBTD//73P+zt7dmzZw+u\nrq7s2rWLr7/++omNu2DBAr7++mueeeYZABo2bMjhw4c1jwswe/Zsjh07xpYtW4D8nsH777+vedzg\n4GDeffdddu/ezZ49e5g6dSqzZs3SPC7AzJkzmTRpkro9Q8OGDdm6davmcWfPno2trS1RUVGsWbOG\nmjVrMnXqVM3jgvmOOSUlBXt7e3bt2oW/vz/r1q3jwIEDmscF853behyzFJJiyMnJAWDPnj106dJF\nvTlTawXTrPWOCxRaURnyp33r4cSJEwQHB1OmTBkAypcvT3Z2tuZx09PTad26tfq4VatWuu2lnZGR\ngbu7e6E2a2trzePa2NhgMBj45Zdf6Nu3L3379lUv7WnNXMecm5vLtWvX2LZtGx07dtQ83p3MdW7r\nccxSSIrB09OTLl26qHf0JyYmqieDljp27GiWuFWrVuXo0aMYDAays7P5+uuv77tRWEmysbEhNzdX\nvXcpMTFRlyJWo0YNPv/8c2JjY4mNjeWLL76gRo0amscFqFChAtHR0eoxb9++HScnJ83j2tnZsXLl\nSjZv3kzHjh3Jy8tTvzRpzVzHPHLkSAYPHkyNGjVwd3cnJiaGZ599VvO4YL5ze9SoUQwePJiaNWtq\ndswy2G5CXl4ex44do06dOjg4OGBtbU16ejppaWm6nPg3b97UPW5iYiLz58/n4MGDKIpC27ZtmTFj\nBhUqVNA0LsDPP//M1q1b+fPPPwkICGD79u2MHz9eHa/RSnJyMsuWLePIkSMAvPjii4wZM0aXGYox\nMTHMnDmTyMhIHB0dqV69OosXL6Z69eqaxk1ISCAsLIwmTZrQokULrly5wh9//IG/v7+mceHex/zR\nRx/h6uqqeWxzMde5feTIEV588UWTbY9CCkkx+Pv7ExoaqnvcjIwM1qxZw9WrV5k7dy6XL1/m0qVL\nvPLKK7rnoqcLFy7w+++/oygKbdq00a03ZG7p6enk5eUVWpLoSRUTE0ONGjUKHXNBm5YuXbrE7Nmz\nuXHjBmFhYURFRfHrr78ycuRITeMWMMe5HRAQQEhIiMm2R/H4bL7+GGvTpg07duygc+fOhZaL0dq0\nadN4/vnniYyMBPL3Xhk3bpzmhWTevHl3tdnb29O4cWO8vLw0jR0dHU2NGjVwc3MjIiKC//3vfzg5\nOeHo6KhJvPnz5zNjxgyGDx9+z+dXrFihSdw73bp1i9DQUOLi4gotP/Tee+9pGnfnzp189NFH3Lhx\nA0VR1OWQjh49qmlcgLFjxxISElJolYxx48axceNGTePOnDmTKVOmqBMpGjZsyOTJk3UpJHqf25GR\nkURGRpKYmMiaNWvU9tTU1BJf5koKSTH88MMPrFmzBhsbG3Xvej3+wUVHR7N06VJ1loetrS16dCAz\nMzO5ePEiXbp0AfL/w6levTpRUVFEREQwY8YMzWKPGTOGDRs28PfffzNr1iw8PT2ZNGkSq1ev1iSe\nn58fAIMGDdLk84tj6NChNG3alPr16+s2qQFg8eLFrFixQtce34ULFzh//jwpKSns3LlTbU9NTSUz\nM1Pz+OYa5Af9z+3s7GzS09PJzc0tNInC3t6+xPeUkkJSDAU9Ar2VLl2a27dvq72g6OhodYMxLZ09\ne5bvv/9e/QfWp08f+vbty3//+198fX01jW1lZYWNjQ07d+7kzTffpF+/fppes2/cuDEAZ86coX//\n/oWe+/e//03Lli01i10gMzOTadOmaR7nnypVqqT7ZcNLly6xZ88eUlJS2L17t9puZ2fH3LlzNY9v\nrkF+0P/cbtmyJS1btiQgIEDzsScpJMUwZswYgoKCaN++va7fGMeMGcPbb7/N1atXmTRpEpGRkbrc\nPJWcnEx6ero63TgjI4ObN29ibW2teSGzsbEhLCyMTZs28eWXXwLoMpMoNDT0rkISEhJyV5sW/Pz8\n+Omnn+jYsWOhn2/BfTxaady4MePHj8fLy6tQ3M6dO2sW08vLCy8vLyIjI2nevLlmcYoSHBzMzJkz\nuXjxIu3bt1cnNujBXOd2VlYWM2fOJC4urlC8b7/9tsRiyGB7MRw4cIANGzZw/PhxunTpQmBgIHXq\n1NEldlJSEsePH0dRFJo2bUrFihU1j7lu3Tq+/PJLWrVqhaIoHDp0iOHDh+Pj48OyZcs0vWnt/Pnz\n/PDDDzRr1ozu3bsTExPDtm3bNNv1MiwsjLCwsLtmsaSlpWFlZcW///1vTeLeae3atXzyySeFrpUb\nDAbCw8M1jVtUL0iPLyuZmZmsX7+ec+fOFbqkpddd5uaY2KD3uV2gR48evP766zRu3LjQF+GC3nhJ\nkELyAFJSUggLC2PFihVUrVqVXr160aNHD023HjYajXcNwr700kuaxbsz7qZNm3BzcyM9PZ0qVapo\nHjc3N5cpU6bw8ccfaxrnTnFxccTGxrJkyRImTZqkttvZ2dGgQQP1zmstderUiXXr1unyJeFxMXbs\nWOrUqUNYWBijRo1i8+bN1KlTR/MJBllZWezYseOub+ejR4/WNK45zu0CgYGBmk9ikEtbxZSUlMTP\nP//Mpk2baNSoET169ODIkSOEhoby3XffaRJz8eLFbNu2jbp16xb6JqH1f+jr1q3j22+/JT4+noYN\nG3L8+HGaNWtWol3he7G2tubKlStkZWXpMhYE4OrqiqurKz/++KMu8e6lVq1aZlkc05y9gujoaD77\n7DPCw8MJCAige/fu9O3bV/O4I0aMwMHBgeeff163cwzMc24XeOWVV1i7di2vvvqqZpdOpZAUw6hR\no7h06RJ+fn6sWLECZ2dnALp160ZgYKBmcX/55Re2b9+u+4n37bffsn79enr37s13333HhQsX+OST\nT3SJXaNGDfr06YOnp2ehqaEDBw7UNO6xY8eYO3cuFy9eJDs7m9zcXGxtbXWZCmtra4u/vz+tWrUq\n9LvW+tv5O++8Q506ddi/f3+hXoEeCnp6jo6O/PXXX1SuXJkbN25oHtdoNOqyXt29mOvcLrhf5M7j\nLulLp1JIiqFfv36F1mG6k5Zdxho1apCdna17ISldurS6FEtWVhZubm5cunRJl9g1a9akZs2aKIqi\n27pPAHPmzOGTTz5h3LhxbNiwgdDQUC5fvqxL7IIBaL2Zq1cA8Nprr5GcnMy4ceMYMWIE6enpjB07\nVvO4zZs35+zZszRo0EDzWP9krnP7119/1TyGFBITUlNTqVat2l3tUVFRmu9jUPBNtU2bNrp+U61S\npQq3bt3Cy8uLgQMH4ujoeM+fgRYKrlWbY1+QWrVqkZubi7W1NT179sTf37/QuIlWAgICyMrKUgtX\n7dq1NR13K2CuXgFAr169gPwpqlpPKrjTkSNHCAkJwdXVtdC/qc2bN2se21znth4rZEghuY+tW7fy\nwQcfUKlSJXJycliwYIF6M9O0adNKdImBe/H09MTT01PTGPfy+eefA/nTj1u1akVKSgrt27fXJfZf\nf/3FlClTSE5OBvLn/S9atIh69eppGtfW1pasrCwaNWrEhx9+iLOzM3l5eZrGLBAREcG7776Lq6sr\niqJw9epVFi1apPlY2L16BePGjdM05p13WN+L1pd5tLr5rzjMdW7rskKGIorUo0cPxWg0KoqiKMeP\nH1e8vb2VnTt3KoqiKH5+fuZM7Yn12muvKQcPHlQf//7778prr72medzY2Fjl9u3bSkpKirJs2TLl\ngw8+UC5fvqx5XEVRlICAAOXChQvq44sXLyoBAQG6xNbbsmXL7vtHKykpKYqiKEpSUtI9/+jBXOd2\nwbl05/9Zvr6+JRpDeiT3kZeXpw6su7u78+233zJ8+HCuXr2q6Zpb48aN49NPPy3yLnI9uuHmYo59\nQXJzc1myZAkff/wxZcqU0Xwq6D9lZ2cXGuSuXbu2LvtUpKSksGzZMnXTslatWjFy5EhN973R+2db\nYNKkSaxcuZLAwEAMBkOhpYb0uGcHzLfnjR4rZEghuQ87Ozuio6OpWbMmAM7Oznz77beMGjWKc+fO\naRa3YC0rPRYMfNwU7AtSsAbWzz//rPmKsOacmgn5N4bNmDGDHj16APlfFEryZrGiTJ8+nXr16vHp\np58C+dvtTps2Td0KVksffvghI0eOpEyZMrz99tucPXuWadOmqb/3krZy5UpAn4Hnopjj3AZ9VsiQ\nGxLvIyoqCltbW2rVqlWoPTs7m23btqn/8EXJ+ee+IC1atGD06NGa7wsyZcoULly4oPvUTMifGbd2\n7dpCx/zGG29oXtT8/PzYtGmTyTYtY+/atYvdu3czbdo0+vbty88//6xJvNOnT9/3+eeff16TuHcy\n17kN2q+QIT2S+yhqVlapUqU0LSLNmze/56UzRcdlvs0lOTlZ81lp92KuqZmQv97SW2+9pRat3Nxc\nsrKyNI9btmxZDh8+TIsWLYD8GU1ly5bVPC7ov430woULi3zOYDBofrMt6H9u/7N4FixOefXqVa5e\nvVqixVMKSTHovW+DuVYbfhxMnz6d+Ph4dde+Fi1a6DLn383N7a6d6rZt26Z5XIABAwawZs0adTro\n7du3GTx4MD/88IOmcWfPns3UqVNJTU1FURTKly9/3/9wS1LBNtJly5Zl9uzZmm8j/d1335GXl0dk\nZGSJ7gz4IPQ+twt+l1lZWZw6dUqNdfbsWRo3blyyqzmU6ND9E8rLy0s5f/68bvGKmlmi5wwTc8rM\nzFQOHz6sfPHFF0qHDh2Ul156SfOY/v7+xWrTQo8ePYrVppWUlBR1VpOekpKSlJycHEVRFCUtLU25\ndu2a5jHNPdvSHOf2qFGjlKioKPXx2bNnlTFjxpRoDOmRFIPe+zbcObPk6tWr6qqwt27domrVqmYd\nMNTa4cOHOXLkCIcPHyYlJYWOHTuql160sHfvXvbt24fRaCy0M2RqaqpuGx7Z2tpy+vRp9VLDqVOn\nNL3EZO57OSD/HO/Zsyfdu3enfPnylCtXrtDYlFbMtdsp6H9uF7h06VKhnk/9+vW5cOFCicaQQlIM\neu/bUFAo3nvvPV599VU6dOgA5P+np+ddwObw1ltv8fzzzzNs2DA8PDw0H3B2cXGhcePG/Prrr4Wu\nGdvZ2em22dT06dMZN24czs7OKIrC9evXNV3bTO8xoHv55JNP2LhxI0FBQTRu3JjAwEDatWun+X/u\nBbudWltbU6ZMGV3HHfU+tws0aNDgrlmBJX1JTWZtFYO59m3w9fW9656Re7U9SW7dusXRo0c5dOgQ\nJ0+exMrKimbNmjF+/HhN42ZnZ6MoChcvXsRgMFC7dm1dpwJnZ2er65nptUTK4yAvL4/du3cze/Zs\nrK2tCQwM5K233tJ8Uy9zMNe5nZmZyffff8+hQ4eA/NXD+/TpU6JjUtIjKQa9Ntv5J2dnZ7744otC\n3yQKbpB8Ujk6OlKjRg2uXr1KfHw8kZGRuuwid+DAAWbNmqXO3IqNjeX9999Xe4NaO3nypLrvzJ9/\n/gmg2Tasd17Cuxe9ZhZFRUWxceNG9u7di7e3N76+vhw5coT+/ftrOgU5PDxcvQmzZcuWJbtUyH2Y\n69wuU6YMAwYMYMCAAZrFkB7JfaxevZohQ4Ywd+7ce3a5tf4Hd/PmTZYvX87hw4cxGAy0aNGCUaNG\nPR4utDcAACAASURBVJHf1gp06tSJOnXq0KJFC1588UXc3d116Rl06dKFlStXqvcMRUdHM3ToULZv\n36557HfeeYeYmBgaNmyojssYDAbNzq+CNeKOHj3K+fPn6datG5C/f7mbmxtz5szRJO6dAgMDcXBw\nICgoCG9v70K/49GjR2t2U+RHH33EyZMn1VUjtmzZQuPGjXVZnNNc5/aRI0dYvnw5V65cKVS4SvQy\neYkO3T9hwsPDFUVRlI0bN97zj17S0tJ0i2Vuf/zxx11thw8f1jxuYGBgocd5eXl3tWmlS5cuSl5e\nni6x7tSrVy8lOztbfZyVlaX06tVLl9jR0dG6xPmn7t27K7m5uerjnJwcpXv37rrENte57e3trezZ\ns0e5fv26kpiYqP4pSXJp6z4KVt4NCAgwS/yjR4/y3nvvkZ6ezp49e4iKiuKHH35g9uzZZslHDx98\n8MFdqyrPmzdPs5WWd+7cCeRPqBgyZAhdu3bFYDCwfft2mjRpoknMf6pXrx4JCQm6X7ZMTk4mNTVV\n7eGmp6erK9NqrUaNGuzZs+eu3Rn1WIvr1q1b6jGnpKRoHq+A3ud2AQcHB80v0UohKYbhw4ff93mt\n1sRasGABX3/9NSNGjADy77QvuLb7pImMjCQyMpLExMRC01NTU1ML7Vdf0nbv3q3+vXLlyuqAZMWK\nFQv9B6elpKQkfHx8cHd3LzTIrvVaa0OHDiUgIIBWrVqhKAqHDh1izJgxmsYsMGvWLG7fvk1ERAS9\nevVix44duhTuYcOG3XXMkydP1jSmuc7tAq1atWLRokV07ty50KU0ubNdZ9WrV+f69evqoPeWLVuo\nVKmSLrvaVa1atdDjO/duf5JkZ2eTnp5Obm5uoemp9vb2fPbZZ5rFNddEijvp9Z/3P/Xs2RMPDw+O\nHz8OwOTJk9VlNLQWGRnJ5s2b8fX1ZfTo0QwcOJAhQ4ZoHrd79+60bNmSkydPAvocs7nO7QIFv99T\np06pbSW+LEyJXih7Qt1rbwg99osYM2aMcuTIEcXf31/JyspSvvrqK2X8+PGaxzWn2NhYRVEUJTU1\nVUlNTdUt7sWLF5W33npL8fHxURRFUc6cOaN8/vnnusU3l/j4eOXIkSPKH3/8of7RQ1BQkKIo+eM0\n8fHxSmZmpuLl5aV53MOHD6tjjqGhocoHH3ygnnNaM9e5rQfpkRRDRkYGMTEx6pLPMTExZGRkaB53\n9uzZzJ8/H6PRiIeHB23btmXWrFmaxzWntLQ0/P39C+0it3DhQurXr69p3JkzZzJlyhT159uwYUMm\nT57MyJEjNYvZp08fvv/++7sW6VR0uklu8eLFbNu2jbp16xbq6Wq9MyPkr7V169YtBg8erK7kEBQU\npHnc2bNn8/PPPxMVFcU333xDUFAQU6dO5T//+Y/msc11bl+/fp0lS5Zw7do1vvrqK86fP09kZKS6\n3XGJMHclswR79+5VOnTooLz55ptK3759lVdeeUXZt2+fpjFzcnKUNWvWaBrjcWSuXeQKZmjduRaT\nnutdmUPnzp2VzMxMc6ehZGZmKrdu3dIlVsH6acuWLVN++umnQm1aM9e5PXjwYGXLli3qrojZ2dkl\nPlNNeiTF4OHhwc6dO7l48SIAderU0Xz+t7W1NZs3b9b0JqLHkbl2katQoQLR0dFqz2D79u26jRcs\nXLiQoKAg6tatq0u8/9feeYdFdW1t/B1BBEGNvUyIldhQYwGDYsNGb5Zg1CsBeyNRkSABVIK9cSVq\nNDERa5QuAjasxIaioEENiiKIioC0oQ7n+4NvzmUUE+519tmTw/49D0+YQ2QtYM1ZZ7f3VaCvr4+K\nigpBT/Ardsm9D1KyQwp0dXXx448/4vjx4zhw4ACqqqoEORQI0KvtvLw8WFpaYvfu3QAATU1Nla+1\nskbyF7yv6NPT0wGQL/qBAwdi9erVsLS0hI6ODn9dCBMeWtBykfP19YW3tzceP36MYcOG4eOPP8am\nTZuIxwWqJey/++47yOVyODo6wtramrg/B1AtFmlvbw8TExOlZkLyoK1il1xOTg4SExP5G+u1a9fQ\nv39/4u+prVu3IioqCv7+/mjdujWeP38OV1dXojEV0Krtxo0bIy8vj39Iun37tsrri51s/wv+TrSP\n9I6f6dOnv3NNKBMeWrztIjdw4EAsWrSImIvc20q4paWlqKqq4pVohVDCVfD48WOEhobixIkTGDBg\nACZNmqT0BKtq3nd+QYhzUy4uLli3bh1/dubVq1fw9PTEzz//TDw2LYSubQX37t2Dn58f/vzzTxgY\nGCAvLw8BAQHvNe77X2CNhFGvUUhxpKWlITk5GaNHjwbHcTh37hz69Okj2KhELpfj3LlzCA0NxYsX\nL2Bubo5bt25BR0eHqBIwLSwsLJSMw6qqqmBlZUXcTExokzp1obKyEmlpaeA4jogoKJva+gto+za8\nT29IiNO/tEhLS8PevXuRmZmpNHdNahSm+F1OnToVoaGh0NPT46/PmTOHSMy3WbNmDc6dOwcTExPM\nnTsXffv25b82fvx4YnGfPHmCLVu2IDU1VenwpRBWBSYmJnB1dYWVlRUAIDo6GkOGDCEed+PGjdi1\na5eg/kIKhK5tBWVlZTh06BBu3rwJiUSCgQMHMvVfIVEcHlI8rSokUxRPq6SpafRTVlaG8+fPo0uX\nLsTj0sTNzQ1OTk6YNGmSoIcvX79+rbROoKWlhdevXwsSu3v37vj6669rNXYKDg4mFtfT0xOLFy/G\nmjVrEBQUhNDQUFRVVRGLVxMfHx+cPn2aVxL44osvMHbsWOJxhTapqwmt2l6+fDl0dXUxbdo0AEBU\nVBTc3d1VehiSNZK/gPbTqouLi9JrV1dXwRYGaaGpqYkvv/xS8Lj29vaYOHEifzM7c+YMHB0dica8\nd+8egOozKwovkpr07t2b6KJ7WVkZTExMAABSqRSLFi2Co6Mj3NzciMVU8Pr1a3To0AF2dnZo27Yt\nWrVqRTwmILxJXU1o1faff/6J6Oho/vXnn3/OKz6rCtZI6gDNp9WalJSU4MWLF4LHFZJRo0bh4MGD\nGDt2rNLvnLR0/rx58zB8+HBey2zt2rXo1asX0Zjr1q1779eE2FShpaWFqqoqdOzYEQcOHEDbtm2J\nuyempKTA19cXhYWFaNu2LQDgxYsXaNq0KXx9fYnvSCwuLoaOjg7i4+OVrgvRSGjVdq9evXD79m18\n9tlnAKolUwwNDVUagy2214GdO3ciJiZG6WnV0tKS+KhE4ZkAVC9G5ubmYsGCBfwQVYwopg9rIpFI\nRG8xTIOkpCR07doVhYWFCAgIQGFhIWbOnMnfcEhgZ2eH1atXo1+/fkrXb9++DR8fH0RGRhKLTRta\ntW1hYYG0tDR06NABAPD8+XN07twZmprV4whVOK6yRlJH7t27xz+tGhkZEX9aBYDMzEz+c01NTbRs\n2ZL/4zPExa1bt3iHRAWkHBJro6qqCjKZjJ++JcW4cePeez5r7NixOH36NNH4ZWVlCA4Ofke+Xh3E\nO0lR8z5SG1Kp9INjiFNKVsWkp6fDwMAAM2bMQPfu3ZGQkICCggLicbOzs9GsWTNIpVK0bdsWpaWl\nvJKnWCkpKcGOHTvg7e0NoHpnUU2pdzHi7u6ODRs24ObNm0hOTkZycrKSUispli5diqKiIshkMlhb\nW8PS0hI//fQT0ZjDhw/H7NmzER0djVu3buHWrVuIjo7G7NmzMWzYMKKxgerfdXZ2Ni5fvgxjY2O8\nfPkSurq6xOMC9GpbKpUiKysLV69ehVQqhY6ODqqqqiCVSlXSRAAwra26YGtry1VUVHBPnjzhxo0b\nx61bt46bOXMm8bh2dnZKznlyuVwwXSBauLm5cbt37+ZVeGUymeg1r2g5JCp+rxEREdzatWu58vJy\nQdwCz58/z3l7e3Nz5szh5syZw3l7e3Pnz58nHpfj/qOlpvg5hXSFpFXb27dv5+bMmcONGzeO47hq\nxWdVa3yxeZI60KBBA2hqauLUqVOYNm0apk+fLsi0A/f/h6Vq5iGULhAt0tPTsW3bNpw4cQJAtYwH\nJ/LZV1oOiZWVlaioqMCZM2cwbdo0NGzYUKneSDFixAjijn3vQzE13LRpUzx8+BCtWrVCTk6OILFp\n1fbp06cRHh7OKxaQ2FTBGkkd0NTURFRUFCIiIrBz504AEOSGrq+vj6CgIEyZMgUAcOjQIUG0eWii\npaWF0tJS/oaWnp4uqKigkCicN4uLi6k4JH7xxRcwMzNDjx49YGRkhMzMTOJrJFVVVQgLC8OpU6eQ\nlZUFDQ0NdOrUCU5OThg8eDDR2ED1z5yfnw83NzfMmzcPMplMkO3OAL3aVjwgKOKSEIpki+11IDU1\nFUeOHMFnn30Ga2trPHv2DDExMZg9ezbRuDk5Ofj+++9x9epVSCQSmJiYYMWKFWjZsiXRuDSJj4/H\nzp07kZqaiqFDhyIxMRFr164V5CYjNNevX//LrxsbGwuUyX+orKwkuqHD09MTHTp0gImJCU6ePAk9\nPT0MGjQIe/bswejRo2vVlxMLtGr7559/xtOnTxEfH485c+YgJCQE1tbWKv1ds0bCUDvy8vJw584d\ncByHfv36oUWLFrRTIsrGjRvh7u7+t9dUjSCGR29hY2OjtN108uTJOHr0KMrLy2FnZ0dca6uwsBDb\nt2/nd2AOHjwY8+fPF0RtGaBX2/Hx8bh8+TIAwNTUFEOHDlXp92e7tuqAmZkZRo8e/c4HadLS0jBj\nxgxYW1sDAO7fv48dO3YQj0uTGzduIDU1Fbq6utDT08OjR494GQ2x8vvvv79z7eLFi8TjfvvttzA1\nNcWrV68AAJ06dSJ+CLJhw4a8DcO9e/f4qTwtLS1B1mdWrFgBPT09BAQEICAgALq6un+r8q0qaNb2\n0KFD4eHhAQ8PD5U3EYCtkdSJkJAQ/vPy8nLExMTwdpkkoWH/SpuaMuJlZWVISkpC7969RSmdf+jQ\nIRw+fBjPnj1TOnxaXFyM/v37E48vhOHR27i7u+Nf//oXtLS0UFlZySsb5+bmYuTIkURjA9XrEtu3\nb+dfL1y4kPcHIY3Qtf22hfPbqFLxmDWSOtC8eXOl187OzoJoEpWUlCgpwQLVzoli5u0F5qysLKxZ\ns4ZSNmSxsbHB8OHDsWXLFixdupS/rqurS1w2AxDG8OhtTExMcO7cOeTl5SlN67Ro0QLLly8nGhsA\ntLW1kZCQgEGDBgEAbt68CW1tbeJxAeFrOzExEQCwbds2tG7dWslQKzs7W6WxWCOpAwpxPaB618nd\nu3cF2bVF0/5VXWjXrh0ePXpEOw0iNGnSBE2aNMGWLVsgl8vx+vVryOVyyGQyyGQyXtKCFN9++y3m\nzZuH9PR0ODk58YZHpJFIJLh27RqGDRsGPT097NixA3/88Qfmz59PXDFi5cqV8PDwQFFRETiOQ7Nm\nzf5S84wkQtV2XFyckvTMl19+CVtbW5U+CLNGUgdqFpqmpiakUim2bdtGPG5t9q8bN24kHpcmfn5+\nfOOsqqpCSkqKIHI0NDlw4AC2b9+OVq1aKU0tqUID6a/o3bs3Dhw4QNTw6H3s2LEDFhYWSEhIwJUr\nV+Dq6gpfX18cO3aMaNyePXsiMjISRUVFAEB8u3NNaNV248aNERkZCSsrK0gkEkRFRdVqWfAhsF1b\n/wBkMhmqqqoELXpa1LR/1dDQgFQqxcCBAylmRJ6xY8fi6NGj70yhCgEtjS97e3uEh4dj8+bN+PTT\nT2FjY8NfIwFtkzqAXm1nZGTA398ft27dgkQiwYABA7BixQp8/PHHKovBRiR1oLCwEIGBgfwOC2Nj\nYyxYsID4fPK+ffswYcIE6Orq4rvvvsMff/yBpUuXwtTUlGhcmgjhF65utGvXTrDtpzVxd3fHs2fP\n0KNHD37tTSKRCNJI2rZtCx8fH8THx2PWrFkoLy8naqpFWh6/Lghd24ot5MnJyfxBalKwEUkdWLRo\nEQwMDPhCiIiIwP37999rhasqbG1tERkZiUuXLuHIkSP4+uuvsXz5cqUnG7FQc9dSbZCe5qHJihUr\nkJaWhpEjRyqddCb9lGxhYYHo6GhBtt2+TUlJCS5duoRPP/0UnTp1wqtXr/Dw4UNRPiTRqm0bGxtE\nRkbC0dGR+D2DjUjqAK0tg4oef+HCBdjb28PAwEC0ulOKHS0HDx4EAKUdJjRudELSoUMHdOjQARUV\nFaioqBAsLi2NL6BaZ6pFixa4efMmOnXqBE1NTXTs2JF43LS0NKxcuRI5OTmIiorC/fv3ERcXR3RL\nPa3aNjU1hZGREWQyGQYMGKB075BIJCrd/svUf+vA5MmTuRs3bvCvExISuMmTJxOP++2333JfffUV\nN3bsWE4mk3GFhYWcg4MD8bg0Uaiz1kTsiscKioqKuKKiIsHiTZs2jRs0aBDn4uLCK/HOmTNHkNhC\nKNLWxtSpU7k7d+4o1ZlCjZc0tGp77ty5xGOwEUkdoLVl0N/fHykpKdDX14eOjg7y8vJEe6ZCAcdx\nuHnzJr8IeevWLaJz5+rAw4cPsXz5cv6Qa/PmzbF+/XoYGBgQjbto0SKi3/+vEEKRtjZons2iVds7\nd+7E69evkZycDABEpFlYI6kDtLYMNmjQAK1atUJqaqrSrhox4+/vjxUrVvC/6yZNmoi+efr4+ODb\nb7/F559/DgC4du0avL29ceTIEaJxaYhCKhBCkbY2aJ7NolXbMTEx2LBhA4yNjcFxHPz8/LB8+XKY\nm5urLAZrJH9BREQE7Ozs3rt1kPRi6MaNGxETE4OuXbsqPTUZGRkRjUuTZs2aITIyEoWFhQCq32zP\nnj2jnBVZZDIZ30SAaiFBkjfWKVOm4PDhw+9IaHD/73+j0rnz92BhYQEfHx8UFBTg6NGjCAkJweTJ\nk4nHre1s1qZNm4jHBejV9s6dOxEcHMyrhufm5sLZ2Zk1EqEoKSkBQG/r4JkzZxAbGytaP47aWLx4\nMcLCwpS2w7q5uSE0NJRiVmTR19fHDz/8oLQIS9J35vDhwwD+I6FBA1dXV8THx0NXVxdpaWlYvHgx\nETHB2vj111+VzmYJ9aBCq7Y5jlOynvjoo49UvmmHNZK/wMnJCXK5HHp6enB2dhY8vr6+PioqKupF\nI3n06BFSU1NRWFiIU6dO8deLiopQVlZGMTPyrFmzBtu3b8fixYsBAAMHDsTatWuJxXvz5s1ffl0I\nnS+gWpG2X79+vNzQmzdviMdW3MxrnuwmfTOnXdumpqZwdXWFlZUVACA6OhrDhw9XaQzWSP4GDQ0N\nREVFUWkkOjo6sLe3h4mJiVIz+e677wTPhTRpaWk4f/48CgsLce7cOf66rq4u/Pz8KGZGnvT0dGRl\nZaGqqgpyuRxXr17F1atXiZ0vcHR0hEQiqfWpVCKR4OzZs0Ti1uTIkSPYvn07GjVqxOdCMjbNmznt\n2vbw8MCpU6dw8+ZNANUukWPHjlVpDHYgsQ6sWbMGlZWVsLS0hI6ODn+9d+/eROO+7xCRmE9/JyYm\nCiKhrk6MHz8eHh4eMDAwUNLakkqlFLMiy7hx43DkyBHBjJ3OnDmDs2fPIi4uDmZmZvx1XV1dWFpa\nYsCAAcRzoFHbcrkczs7O2L9/P9E4bERSB1JSUgBASRlVIpEQ98gQc8N4H6dPn4aBgQEaNWqEmTNn\n4sGDB/D09BTMM4IGLVq0ULq5CcWMGTOwb9++v71GAsWWdqEYM2YMxowZQ/VBhUZta2hooEGDBigs\nLCQqw8MaSR0g3c3fh5mZWa0nX4WYeqBFfHw8li9fjtOnT0MqlSIwMBBTp04VdSNZvHgxvLy83pnC\nHDduHJF4ZWVlkMlkyMvLQ35+Pj/FVVRUhJcvXxKJ+TZLly6Fk5MT+vXrJ+i0ba9evXDw4EH8+eef\nSlNaJNekFNCq7caNG8PGxgZDhgxRWhtS5e+aNZI6QMPbGqDnzEgTxcLr+fPnYW5uTkXMUGhCQkLw\n+PFjVFZWKk1tkWokR44cwb59+/Dq1SulUa+enh6mTZtGJObb+Pj44PPPP8enn35K3JWxJu7u7ujS\npQsuX76MBQsW4Pjx4+jSpYsgsWnV9rhx44jVEg/xs/MiwNXVlTtx4gRnY2PDcRzHVVRUcNbW1lRy\nEbtEysaNG7nx48dzdnZ2XHl5OZeTk8NNnDiRdlpEUciECE1QUBCVuBxXu1yIkHEV79/y8nJu0qRJ\ngsSmWdslJSXco0ePiH1/NiKpAzS8rQF6zow0WbZsGWbOnIkmTZpAQ0MDOjo62LFjB+20iDJgwACk\npqaiW7dugsS7cuUKTExM0LZtW6UdTAqIP70CGD58OH777TeMGjVKaWqL9PZfTc3qW17Tpk3x8OFD\ntGrVCjk5OURjKqBV23FxcVi/fj0qKioQFxeHlJQUBAQEvGP9+yGwRlIHaHhbA+86M3788ceCODPS\n5tWrV/j9999RXl7OXxPCI4MWt2/fhr29PaRSqdJNldT23xs3bvDe6bUhRCOJiooCAPz444/8NSG2\nHn/xxRfIz8/H119/jXnz5kEmk6nUcvbvoFHbgYGBCA4OxvTp0wFUSz5lZGSoNAbb/lsH7t27Bz8/\nP/z5558wMDDgva179OhBJN6+ffswY8YMJCQkYNCgQURiqCuBgYG4du0aHj16hBEjRuDixYsYOHAg\n/v3vf9NOjRiZmZm1Xhfz9l9aPHv27B3VgNqukYBWbU+ePBlHjx5VcqC0sbFR6YMKG5HUgfT0dPz0\n00/IysrCyZMnkZSURFREMTQ0FDNmzIC/v78oTaz+ipMnTyIiIgL29vZYu3YtXr9+DXd3d9ppEUXo\nhkHTdlYxrVbblBpAfjSkONleE6EkeGjVdrdu3XD8+HHI5XI8efIE+/fvV/kWaNZI6sCOHTtgYWGB\n/Px8XLt2Da6urli5ciWOHTtGJF7Xrl0xbtw4vHr1qlZ3NTG7BTZq1AgNGjSApqYmioqK0LJlS2Rl\nZdFOS1QotOPS0tKQnJzMn2E5d+4c+vTpQzQ2rWk12jIlAL3a9vb2xq5du6ClpYUlS5Zg2LBhKjfy\nYo2kDiiUdy9cuIDJkydj5MiRRNcqtmzZguzsbLi6uhL3WlY3DA0NUVBQgEmTJsHR0RGNGzeudyfd\nSbNw4UIAwNSpUxEaGsrbIixcuBBz5swhGluhJzZ//vxap5hIQVumBKBX2zo6Ovjmm28wa9YsAGRs\nMNgaSR2YM2cO2rZti/j4eISFhUFbWxsTJ05EZGQk8djl5eV48uQJAKBz585o2LAh8ZjqQkZGBoqK\nioitRdV3xo8fj+PHj/ML/OXl5bCxscHJkyeJx3ZwcHhnisnR0ZH4FJO6SPAIWdtJSUnw8vLiR6J6\nenpYs2YNDA0NVRaDjUjqwLZt23Dp0iW4uLigadOmePXqFZYvX0487vXr1+Hh4QGpVAqO45CVlYX1\n69eL2o8EAC8wJ5FIMHDgQNZICGFvb4+JEyfyAn5nzpwhLstDe4qJtgQPjdr28vKCr68vv3EnISEB\nnp6eKp0iZyMSNcbR0RGbNm3iT96mpaVh6dKlovbmWLlyJdLT05Ukrz/55BP4+vpSzkyc3Lt3DwkJ\nCQCqDdN69epFNB5t8UQ7OztERETg9OnTOHfuHDw9PTF16lRBZhdo1XbN3VoKahsRfghsRKLGVFRU\nKMk3dO7cGRUVFRQzIs/Vq1cRExPDn9lxcHDg33gM1VNSUgI9PT1MmDABubm5xLfCJiYmYu3atQgM\nDOTXaoSEpgSP0LWtONBsZGQEHx8fWFlZQSKRIDo6WuU2y6yRqDGGhobw8vKCra0tgOrdWqqc11RH\nOnbsiOfPn/NbYrOystCxY0fKWYmTwMBA3L17F2lpaZgwYQIqKirg7u5O1Cv+4sWLWLZsGc6ePUul\nkYwaNQrm5ubQ1tbGypUrkZubi0aNGgkSW+jarnmgGaj+eyuoTQz2Q2BTW2pMeXk5Dh48yBvSDBo0\nCF9++aWoHROnTZuG5ORk9O3bFwCQnJwMQ0NDfqeJKmUd6jt2dnYIDw+Hg4MDsYNqb7N+/XocO3YM\nMpkM2travKEVJ6Bf/Js3b3iZEplMhuLiYrRu3Zp4XDHXNhuRqDFaWlr46quviB4QUzcU20MZ5GnY\nsCEkEgn/dCqTyYjH9PDwgIeHB+bNm0dla3tJSQkOHTqErKws+Pn54dWrV0hLS8OoUaOIx6ZV2wUF\nBQgPD0dmZqbSQWomI19PuHnzJgIDA/H8+XMlsUYx+5EYGhpCW1sbDRo0QFpaGh4/fozhw4fXq23P\nQmFhYQEfHx8UFBTg6NGjCAkJweTJkwWJvXPnTrx+/RrJyckAgH79+gnilujp6YnevXsjMTERANC2\nbVu4ubkJ0kho1fbs2bPRr18/opL9bGpLjTE3N4enpycMDQ2VCqB58+YUsyKLo6MjDh48iIKCAkyZ\nMgWGhoZo2LAhNm/eTDs1URIfH4/Lly8DAExNTTF06FBB4sbExGDDhg0wNjYGx3FISEjA8uXLYW5u\nTjSu4qxKzZ1Mtra2guzaolXbqt6hVRtsRKLGNGnSBCNGjKCdhqBwHAcdHR0EBwdjypQpmDVrFr/Z\ngKE6anp5C9U8arJz504EBwejZcuWAIDc3Fw4OzsTbyRaWlooLS3lp/PS09MFW3OkVdt2dnY4evQo\nRo4cSUyynzUSNWbw4MFYv349xo0bp1QAvXv3ppgVWTiOQ2JiIo4fPw5/f3/+GkO1COXl/T44juOb\nCFB9UxPi77xo0SLMnDkTWVlZWLp0Kb8dWQho1XbDhg2xYcMGpcV8VUv2s0aixty5cwcAcPfuXf6a\nRCJBUFAQrZSI4+XlhR9//BFjxoyBgYEBnj17hsGDB9NOS5QI4eX9PkxNTeHq6qp0OG/48OHE4w4d\nOhS9evXCnTt3wHEcvLy8BFmbAejV9t69e3Hq1CmiPydbI2GoFQ8ePED37t1pp1EveN+8OWmZFAUK\nuRCgemu7QqqFBDXdRmtDiFE+rdp2cXHBDz/8AB0dHWIxWCNRYwoLCxEYGIgbN24AAIyNjbFgCs3R\ndwAAIABJREFUwQIqUxFC8eWXX6K8vBwODg6wtbUV9c9KG5lMhkaNGvHq1nK5HOXl5URvOIo4ivUZ\noVC4A9aGUKN8WrW9YMECpKamYvDgwUpT5KocebJGosYsWrQIBgYG/BNiREQE7t+/r3RCVYykpaUh\nNDQUsbGx6Nu3LxwcHGBqako7LdExefJk/PLLL9DV1QVQ7VPi6upK9GS7ghkzZiAwMLDePSjQqG0h\nRp6skagxCoG5v7smRuRyOc6cOYPvv/8eenp64DgOS5YsEcRPvL5As77mzZuHlJQUKuszDx8+RGpq\nqqC+6TURY22zxXY1RltbW8m3/ebNm9DW1qacFVnu37+P0NBQXLhwAUOGDMGuXbvQu3dvvHz5Ek5O\nTv/oN5u6oaOjg3v37vHrA3fv3hWsvsaNG0flb/k+33QhGgmt2jYzM6tVW0uVu7bYiESNSUlJgYeH\nB4qKisBxHJo1a4Z169aJ2p9j2rRpmDhxIi+sV5Pw8HBBnxzFTlJSEpYsWYI2bdqA4zi8fv0aW7du\nFUwYtLS0FM+fP1dSuCaNjY0N75seGRnJ+6b/nY+9KqBV23l5efzn5eXliImJQX5+Ptzc3FQWgzWS\nfwBFRUUAyFhkMuo3FRUVSEtLAyCsA2dcXBzWr1+PiooKxMXFISUlBQEBAcSFCydOnIjg4GA4Ojoi\nKCgIurq6sLCwQGxsLNG46oaq3SjZ1JYaEhERATs7u/c+JYlZxPHJkyfYsmULUlNTlRzzxKwvRpPk\n5GRezO+PP/4AIMx6QWBgIIKDg/ndVD179kRGRgbxuLR80wF6tV1z63NVVRXu3r2rpN2nClgjUUNK\nSkoAgPdYrk94enpi8eLFWLNmDYKCghAaGoqqqiraaYkSd3d3PHv2DD169OC3AEskEkEaiaam5js7\ntlTtkVEbK1euBABMmTIFw4YNE8w3HaBX2+vWreN/t5qampBKpQgICFBpDNZI1BAnJycAgImJCQYO\nHKj0NcUBLrFSVlYGExMTAIBUKsWiRYvg6Oio0vlcRjV3795FdHS0IDfwt+nWrRuOHz8OuVyOJ0+e\nYP/+/YKMDDiOQ2RkJJ49e4aFCxfi+fPnSEpK4j1CSEKrtn/66SecPHlSSUb+xIkTKjUWI6MpzFAJ\n33//fZ2uiQktLS1UVVWhY8eOOHDgAE6fPl0vR2ZCYGBggOzsbCqxvb29kZqaCi0tLSxZsgR6enrw\n8vIiHnflypW4ffs2Tpw4AaDaK37VqlXE4wL0anv+/Pk4d+4cNDU10bhxY/5DlbDFdjUkMTERiYmJ\n2LdvH5ydnfnrRUVFOH36tCCS17RISkpC165dUVhYiICAABQWFmLmzJn47LPPaKcmOqZPn4779++j\nb9++SovsQjr1Cb2RRCGpTkNGnlZtW1tbIyoqimgMNrWlhlRUVEAmk0Eulys9sejp6eHf//43xczI\no5hi0NXVFUyVtb6yaNEiarGTkpLg5eXF17eenh7WrFlDfOuxpqYm5HI5P52Xm5tLzOzpbWjVdv/+\n/YnrfLERiRqTmZkJqVRKOw1BmDt37l9+/Z/sZ63OZGZm4unTpxgyZAhKSkogl8sFGR3Y2NjA19eX\nP2ybkJCAVatWEfWLB4DIyEhER0fjjz/+gIODA2JjY/H111/DwsKCWEzatW1paYn09HRIpVIlrS1V\n/q7ZiESN0dHRwfr169/ZLihGGXkXFxcA1Yqwr1+/5g1/Tpw4oeRbwVAdR48exW+//Yb8/HycOXMG\nL1++hK+vL/bt20c8toaGBt9EgGr1X01N8rcjW1tb9O7dG1evXgXHcdixYwe6du1KNCbt2t6zZw/x\nGKyRqDHLli2DhYUFzp8/j1WrViEsLEww7wShMTY2BlC9VbHmQSkzMzM4OjrSSkvUHDx4EMeOHeN9\n2jt16oTc3FyiMRVnGoyMjODj4wMrKytIJBJER0fzNUAKuVwOKysrxMbGEm8eNaFd20LMarBGosa8\nefMGkyZNQlBQEIyNjWFsbIwJEybQTosoJSUlePbsGfT19QEAz54948/VMFSLlpaW0lSHqg+p1ca6\ndeuUXtdUsia9DVlDQwOdO3fG8+fP0aFDB6KxakPMtc0aiRqjGOq3adMG58+fR5s2bZCfn085K7J4\nenpi+vTp0NfXB8dxeP78OVavXk07LVFiZGSEXbt2obS0FPHx8Th06BDMzMyIxhTSg6Q2CgoKYGVl\nhb59+yr5rgixBifm2maL7WrMuXPnMGjQIGRlZcHPzw/FxcVYsGABRo8eTTs1opSXl+Px48cAgC5d\nuig9NTNUR1VVFYKDg3H58mUA1fa3kyZNEuSAYkFBAcLDw5UOyQHkZeSvX79e63XS02oKxFrbrJGo\nMVlZWWjfvr3StezsbLRu3ZpSRmQpLi7GpUuX8OLFCzRo0ACdOnWCqampYNsz6yOKG5tEIkHnzp0F\nu7E5OTmhX79++PTTT5X+viRtfmk4MyoQe22zRqLG9OrVC+bm5vD39+eH4YoDVWIjOjoae/fuRffu\n3XHt2jX0798fVVVVePjwITZu3Chq6XxanD9/Hr6+vvjkk0/AcRwyMjKwatUqjBgxgnhsWnVMw5mx\nXtQ2x1Bb7OzsuAMHDnD29vbc06dP+WtixNrampPJZBzHcVxOTg7n4uLCcRzHpaSkcF988QXN1ETL\n+PHjuSdPnvCvnz59yo0fP16Q2L/88gv322+/cS9fvuTy8vL4D9LMnTuXGzFiBOfp6cn5+fnxHySp\nD7XNFtvVGIlEgqlTp6JHjx6YO3culi1bRkVgTygUZj+NGzdGTk4OAKBHjx68jAZDtejq6qJjx478\na319fd6/nTQNGzbEhg0blBa5JRIJcUl1Ws6MYq9t1kjUGO7/Zx0HDhyIX3/9FV9//TW/UCc2hg8f\njpkzZ2LQoEG4dOkSzM3NAVRvgebY7CsRDA0NMWvWLFhYWEAikSA2NhZ9+vTBqVOnAIDoDXfv3r04\ndeqU4OeiHBwcBHdmrA+1zdZI1JhXr16hTZs2/OvKykokJibCyMiIYlbkuHDhAlJTU9GjRw8MHToU\nQPXOosrKStHsblEnPD09//LrJPWgXFxc8MMPPyhtwRUCWs6MYq9t1kjUkPrskMioHyxYsACpqakY\nPHiw0o2U9PZfR0dH7Nu3D9OnT+fVf4VQx62srOTPhRUXF+Px48fQ19fHRx99RDSuUIhj75nIqOmQ\nWNuHGAkODuY/f/HiBWbMmIFBgwbBycmJ9xRnqJYXL15gwYIFMDExgYmJCRYtWoQXL14IEnvMmDGY\nO3cu+vfvj969e/MfpKHhzBgaGoqhQ4di/PjxuHDhAmxtbbFp0ybY2dkRb2CCQXGhn8Hgsbe35z9f\nvHgxd+TIEU4ul3OnTp3i/vWvf1HMTLw4OztzwcHBXEVFBVdRUcGFhIRwzs7OtNMiiqenJxcZGclZ\nW1tzaWlp3OrVqzlvb2+iMa2trbmcnBwuPT2d69+/P78DMzs7m7O2tiYaWyjYYrsa8ncuiKSH/7R5\n8uQJ7yk9duxY/PDDD5QzEie5ublK2m2KaR8hMDMzq3UkQHrXlre3N3bt2sU7Mw4bNgzz588nGrNB\ngwZo0aIFWrRogcaNG+OTTz4BALRq1YpoXCFhjUQNEWKIr268ePEC33//PTiOQ25uLioqKnjXPiHE\nBOsjH330ESIiImBtbQ0AiIqKEmzOPiQkhP+8vLwcMTExgujI6ejo4JtvvsGsWbMACOPM2L59e2ze\nvBnFxcXo0qUL1q1bh7Fjx+LKlStKm2n+ybDFdoZa8PYpZzMzMzRr1gzZ2dnYv38/lixZQikz8ZKZ\nmQk/Pz/cvn0bEokE/fv3x3fffUdFGReoHhHVlFknAQ1nxqKiIhw8eJA/F3bp0iWEhYWhffv2mD9/\nviiaCWskaoi/vz+8vLze66zG3AIZH8LGjRvh7u6OmJgYos6Af4XClwSo3gZ79+5dHD58mLh3Oi1n\nRrHDprbUEDs7OwD/cVarD1RWViI4OJh36gOAtm3bYvTo0Zg4cSI/zcX4cC5evIhly5Zh9+7d1BrJ\nunXr+DUSTU1NSKVSfl2MJDScGQsLC/Hjjz/izJkzyM3NhUQiQYsWLTB69GjMnj0bTZs2JRpfCNiI\nRI3Zt28fZsyY8bfXxMCSJUvQpEkTODg4oF27dgCq103CwsKQn5+Pbdu2Uc5QPKxfvx7Hjh2DTCaD\ntra20ulqiUSCW7duEc+hrKwMJ0+efEdGfuHChUTiKUZA4eHhKCsrU3JmbNSo0d8ezvwQXF1dMXjw\nYDg4OPDK3dnZ2QgLC8PVq1exd+9eYrGFgjUSNaY2hVR7e3v+IJWYGD9+PE6ePPlff43xvzNv3jzs\n3LmTSmxXV1c0bdoUvXr1goaGBn+d1Ch8+vTp7/2aRCJBUFAQkbhA/ahtNrWlhkRFRSEqKgoZGRlK\n6yTFxcVo1qwZxczI0axZM8TExGD8+PG8R0NVVRViY2NFMfRXR3bu3InXr18jOTkZANCvXz/BtK9e\nvnyJn3/+WZBYAF1nRqlUij179sDBwYHf8vv69WuEhoa+4zf0T4WNSNSQzMxMZGRkYMuWLVi6dCl/\nXVdXF927dyc+p0uDjIwMbNq0CVevXkWzZs3AcRwKCgrw+eefY+nSpbzPNUN1xMTEYMOGDTA2NgbH\ncUhISMDy5ct5UUGSeHt7Y9q0aejevTvxWDWh4cyYn5+P3bt34+zZs8jNzQXHcWjVqhXMzMwwa9Ys\nUciksEbCUDvy8vIAAM2bN6ecibixtbXFL7/8gpYtWwKoPqDo7OxMfOcUAFhaWiI9PR1SqVRJa4v0\n7ikazoz1AfE92oqI/v378ztbKioqUFlZCR0dHUEWQ2mQlJQEAOjbty9SU1MRHh6OLl26COLYVx/h\nOI5vIkD1AUWhniv37NkjSJy3KSsrI7qwXht37txB165doaenh9LSUuzevRt//PEHunbtirlz5wrq\n1kgKNiL5h8BxHM6ePYvbt29j2bJltNNROYGBgbh48SIqKysxdOhQ3LlzB4MHD8bvv/8OU1NTzJs3\nj3aKomP9+vV4+PAhrKysAFRbwnbv3h3u7u6UMyPHr7/+isaNG2PkyJFKIyGS00tWVlaIiIiApqYm\nvL29oa2tjfHjx+Pq1au4f/8+AgMDicUWCtZI/mGIddeWjY0NwsPDUV5ejqFDh+LixYv8E9ykSZPY\ngTFCnDp1Cjdv3gRQfaZi7NixlDMiy8GDB7F161alDRyknRktLCwQExMD4N2dmHZ2doiIiCAWWyjY\n1JYao3CqA/5z+rdRo0YUMyKHhoYGNDQ0oKOjg08++YTXQNLW1laay2aoBrlcDmdnZ+zfv5+K9Swt\naDgzGhgYICQkBBMmTECPHj2QnJyMPn36IC0tTTQbZ8TxU4iUc+fO8Z9raGhAKpVix44dFDMiR8OG\nDVFSUgIdHR0lvaXCwkLWSAigoaGBBg0aoLCwUBRz9HWlY8eOgrsy+vv7w9/fHzt37kTz5s3h5OSE\ndu3aoX379vD39xc0F1KwqS2GWlBeXl6r5Whubi6ys7MF3yZaH5g3bx5SUlIwZMgQNG7cmL8uZpsC\nWs6MQLV4Y0ZGBiorK9GuXTsmI88Qhtp8SfT09GBoaIgxY8ZQyIgcNd/UCQkJePr0Ke+VUfMmx1Ad\n48aNq1fTWkC1MyOt946enh6Kiorw9OlTGBoaIjc3F8XFxaI4I8VGJGqMt7c3Hj9+zB8QO3XqFD7+\n+GPk5eVBX18fXl5elDNUPYGBgbh79y7S0tJw8uRJvHz5Em5ubjhy5Ajt1ERJaWkpnj9/ji5dutBO\nRfSIubbZiESNefDgAQ4fPsxrEU2ZMgVTp07FoUOHYGNjQzk7Mpw+fRrh4eH8AbG2bduK1qeeNnFx\ncVi/fj0qKioQFxeHlJQUBAQEiNqmgJYzIyDu2maNRI3Jz8+HTCbjF0NLSkrw5s0baGho1LqeIAYa\nNmwIiUTCv9llMhnljMRLYGAggoODeUHDnj17IiMjg3JWZKHlzAiIu7ZZI1FjZs6cCTs7OwwePBgc\nx+HGjRuYO3cuZDIZTExMaKdHBAsLC/j4+KCgoABHjx5FSEgIJk+eTDstUaKpqfnOjq3antbFxNuy\nO87OznB0dISbmxvx2GKubbZGoua8evWKlw7p06cP2rZtSzkj8sTHx+Py5csAAFNTUwwdOpRyRuJk\nxYoVMDExwe7du7F9+3bs378fFRUVWL16Ne3UiEHLmVGBWGubNRI15+XLl+8olRoZGVHMSBiKiopQ\nWVnJvxaDQqq6UVJSgl27duHy5cvgOA7Dhg3D/PnzRXvoFaj2JXnbmdHFxQWdO3cWLAcx1jZrJGrM\nxo0bERMTg27duikdyhPzYuiRI0ewfft2NGrUCBKJBBzHEZewqO8UFRUBAK8mIGaEdmasiZhrm62R\nqDFnzpxBbGysaBfWa2Pv3r04fvy4oBIW9ZWkpCR4eXnxO4f09PSwZs0aGBoaUs6MHPPnz+edGYUe\neYm5tlkjUWP09fVRUVFRrxqJvr6+4BIW9RUvLy/4+vpi0KBBAKoPgnp6eopaIFNoZ8aaiLm2WSNR\nY3R0dGBvbw8TExPB5RxosXTpUt58qL78zLTQ0NDgmwhQrf4rFhHB99G/f388ePCAiuSOmGtb3FXz\nD8fMzAxmZma00xAUHx8ffP755+842DFUh2LnkpGREXx8fGBlZQWJRILo6GgYGxtTzo4sN2/eRFhY\nmODOjIC4a5sttjPUCrH6ragTigOItSGRSBAUFCRgNsKSmZlZ63WpVEo8tphrmzUSNcTNzQ0BAQHv\nlUER8xz2li1bIJVKMWrUKMEc7BgMIRBzbbNGooa8evUKbdq0ofr0RIvapvLEskVS3SgoKEB4ePg7\nW2HFMGevjoi5tlkjUWN++eUXWFpa1ovT7AzhUSz8vj1nrxAVZDDqCltsV2OKi4vh4uKCZs2awdLS\nEubm5qIyw6nJlStXYGJiomQvXJP65pshBGVlZfD09KSdhuipD7XNGokas3DhQixcuBD3799HTEwM\npk2bhnbt2uHXX3+lnZrKuXHjBkxMTJTshWsihjebumFnZ4ejR49i5MiRopuzVyfqQ22zqa1/ANnZ\n2YiNjcWJEydQXFws6sX2Z8+eveMYV9s1xodz8OBBbN26FU2bNuWviWXOXh0Rc22zRqLGHDx4ELGx\nscjNzYW5uTksLCzQrVs32mkRxcHBAWFhYUrXHB0dERoaSikj8TJ69GgcO3ZMlJId6oiYa5tNbakx\nL168wIoVK9CzZ0/aqRDn0aNHSE1NRWFhodJcclFREcrKyihmJl46duwoWskOdaI+1DZrJGrImzdv\nAACurq5KrxWIcQ47LS0N58+fR2FhodJcsq6uLvz8/ChmJl4UEjyDBw8WnWSHOlEfaps1EjXE0dGR\n90x4e+ZRrHPYiYmJWLt2LQIDAwWR9GYAY8aMwZgxY2inIXrqQ22zNRKGWmBjY4PIyEg4Ojq+M4/M\nYPyTqQ+1zUYkas7Zs2eRkJAAADA2NsaoUaMoZ0QGU1NTGBkZQSaTYcCAAbzpj+K/t27dop2i6DAz\nM6vVo12MI16a1IfaZiMSNWbTpk1ITk7mNbdOnDiBPn36YMmSJZQzI8e8efOwc+dO2mnUC/Ly8vjP\ny8vLERMTg/z8fLi5uVHMSryIubZZI1FjbGxsEBERwctXyOVy2Nvbi/ocCQC8fv0aycnJAIB+/fqx\n7akCIpbtqOqKWGtbXKL4IqSgoID/vLCwkGImwhATE4NJkyYhNjYWMTExmDhxImJjY2mnJUru3bvH\nfyQnJ+Pw4cOorKyknZZoEXNtszUSNWbOnDlwcHDA4MGDwXEcbty4gWXLltFOiyg7d+5EcHAwWrZs\nCQDIzc2Fs7MzzM3NKWcmPtatW8evkWhqakIqlSIgIIByVuJFzLXNGokaY21tDWNjY34ovGzZMrRu\n3ZpyVmThOI5/owHVZ2bY7CsZfvrpJ5w8eVJJRv7EiROi3aJKGzHXNmskaszcuXNhbW0NMzMzNG7c\nmHY6gmBqagpXV1dYWVkBAKKjozF8+HDKWYmT+fPno2nTpujVqxcaNWpEOx3RI+baZovtasz169cR\nHR2NCxcuoE+fPrC0tMSoUaNE/6Y/deoUbt68CQAYNGgQxo4dSzkjcWJtbY2oqCjaadQrxFrbrJH8\nA5DL5bh69SqOHj2KS5cuiWLfeW3I5XI4Oztj//79tFOpF3h7e2PatGno3r077VREj9hrm01tqTml\npaWIi4tDTEwM7t27J2r3Og0NDTRo0ACFhYVo0qQJ7XREz82bNxEWFgapVKqktSX27eU0EHtts0ai\nxri5uSE5ORmmpqaYOnUqjI2NlSxRxUjjxo1hY2ODIUOGKK0LMSFB1bNnzx7aKdQrxFzbbGpLjbl0\n6RKGDBkCDQ0N2qkIxvu0iMQ8EmPUD8Rc26yRqDElJSX45ZdfkJWVBT8/Pzx58gRpaWmi1dtSUFpa\niufPn6NLly60U2EwVIpYa1vc8yT/cDw9PdGwYUMkJiYCANq2bYtt27ZRzooscXFxsLOzw8yZMwEA\nKSkpmDt3LuWsGIwPR8y1zRqJGpOeno5Zs2ZBU7N6KUtHR0c0B5jeR2BgIIKDg3kf8Z49eyIjI4Ny\nVgzGhyPm2maNRI3R0tJCaWkpL2ORnp6utLtGjGhqar6zq6U2qXMG45+GmGub7dpSUziOg5OTE2bO\nnImsrCwsXbqUd1oTM926dcPx48chl8vx5MkT7N+/H/3796edFoPxwYi5ttliuxpjY2ODoKAg3Llz\nBxzHiUp2+n2UlJRg165duHz5MjiOw7BhwzB//nzRn+ZniB8x1zZrJGqMh4cHpk6dir59+9JORXCK\niooAAHp6epQzYTBUixhrmzUSNcbc3Bzp6eno0KEDdHR0+OtiPnmclJQELy8vFBcXA6h+s61ZswaG\nhoaUM2MwPgwx1zZrJGpMZmZmrdelUqnAmQiHjY0NfH19MWjQIABAQkICVq1aJermyagfiLm22WK7\nGiPmhvE+NDQ0+DcaUK2Qqtj+zGD8kxFzbbMRCUMtuHfvHgAgPDwcZWVlsLKygkQiQXR0NBo1agRP\nT0/KGTIY/xv1obZZI2GoBdOnT3/v1yQSCYKCggTMhsFQHfWhtlkjYTAYDMYHIY4JOoZoKCgoQHh4\nuJKPOCAOqW1G/UbMtc0aCUOtmD17Nvr164dPP/1U9N4rjPqFmGubNRKGWlFWViaKxUcG423EXNsa\nK1euXEk7CQZDQWlpKR4+fIjWrVujoqICpaWlKC0thba2Nu3UGIwPQsy1zRbbGWrFwYMHsXXrVl5q\nG6je2XL27FmKWTEYH46Ya5s1EoZaMXr0aBw7dkz04pSM+oeYa1tcKz6MfzwdO3ZU0hVjMMSCmGub\nLbYz1AodHR3Y29tj8ODBSiZeYtgiyajfiLm2WSNhqBVjxozBmDFjaKfBYKgcMdc2WyNhMBgMxgfB\nRiQMtcLMzKxWH2sx7Gxh1G/EXNuskTDUipCQEP7z8vJyxMTEID8/n2JGDIZqEHNts6kthtrj6OiI\n0NBQ2mkwGCpHLLXNRiQMtULh3QAAVVVVuHv3LiorKylmxGCoBjHXNhuRMNSK6dOn8/PImpqakEql\ncHFxQefOnSlnxmB8GGKubdZIGGpFWVkZTp48+Y7U9sKFCylmxWB8OGKubTa1xVAr5s+fj6ZNm6JX\nr15o1KgR7XQYDJUh5tpmjYShVrx8+RI///wz7TQYDJUj5tpmWlsMtaJ///548OAB7TQYDJUj5tpm\nayQMtcLS0hLp6emQSqVKekTHjx+nmBWD8eGIubZZI2GoFZmZmbVel0qlAmfCYKgWMdc2ayQMBoPB\n+CDYGgmDwWAwPgjWSBgMBoPxQbBGwmAwGIwPgjUSRr3BzMwMlpaWsLOzw/jx47FgwQIkJibW6d+G\nhYXh6dOn/3PsR48eoUePHti3b5/S9TNnziA5OZl/nZmZiaNHj/7l97p79y7c3d35///zzz//r/Op\nSxwGo66wRsKoV2zfvh0RERE4efIk7O3tMXv2bCQlJf3tvwsNDcWTJ0/+57ghISEwMTF5R+n17Nmz\nSvEzMjLw22+/vff7yOVyGBoaYuPGjfy12jwu/o6/i8Ng/DewRsKoV9TcpDh27Fg4OTlh7969AIAr\nV67AyckJjo6OsLW1RXR0NIDqJnL37l18//33cHBwwJUrV/Dw4UNMnToVjo6OsLa2RlBQ0HtjyuVy\nREZGYvXq1SgtLcXdu3cBAJcvX0ZcXBz27NkDBwcHREREwM/PD48fP4aDgwPc3NwAVI+kNm/ejEmT\nJsHX1xfXr1/HhAkTlH6m9evXw9bWFra2tkhISACAd/6/mq9ri/P48WPMmjULkyZNgr29PcLCwgAA\npaWlcHNzg7W1Nezt7fHNN9982B+BITqYRAqjXtOvXz+cO3cOAGBoaIjDhw9DIpEgJycHjo6OGDZs\nGBwdHREWFoaZM2dixIgRAACZTIZff/0VDRs2hEwmw6RJk2BqaoouXbq8E+P8+fPo3Lkz9PX14ejo\niODgYBgaGsLU1BRmZmYwNDTE1KlTAQDt27fHhg0bEBwcrPQ9iouLcezYMQDVDaHmKOTNmzfo2bMn\nPDw8cP36dSxduhRnzpwB8O5oRfHax8dHKY5cLseyZcuwefNmdO7cGcXFxZgwYQI+++wzPHr0CMXF\nxYiKigIAFBYWftgvnSE6WCNh1GtqjlBycnLg6emJp0+fQkNDAwUFBUhLS0Pfvn3f+XclJSXw9fXF\n/fv30aBBA2RnZ+P+/fu1NpKQkBA4ODgAAGxtbWFvb48VK1YonW7+O+zt7d/7NS0tLdja2gIAjI2N\noa2tjbS0tDp/bwB48uQJHj9+jCVLlvC/k4qKCjx69Ajdu3fH48eP4efnByMjI4wcOfK/+t4M8cMa\nCaNek5SUBAMDAwDAypUrMXr0aAQGBgIAxo8fj7Kyslr/3ZYtW9C6dWts2LABEokErq6pa52sAAAC\nN0lEQVSuKC8vf+f/y8nJweXLl3H//n388MMP4DgOZWVlOHXqFKytreucZ+PGjf/rn01DQwNVVVX8\n6/f9LEB1Q23RogU/nfU2UVFRuHLlCi5cuICtW7fi+PHj/1UjZIgbtkbCqLecOXMGv/32G1xcXABU\nT9ko5Cri4+ORnp7O/796enpKUzqFhYVo3749JBIJHj58yK9LvE1YWBjMzc0RFxeHs2fPIi4uDv7+\n/vyUkq6uLoqKit4bpy6Ul5fzek0JCQkoKytDly5doK+vj4yMDBQWFoLjOJw4ceK9cTp37gxtbW1E\nRETw1x4/foyioiK8fPkSDRo0wOjRo+Hp6Ym8vDzReI0zVAMbkTDqDRKJBIsXL0bDhg1RWlqKrl27\nYs+ePejTpw8AYOnSpVi1ahW2b9+OPn36oEePHvy//eKLL7Bu3Tr8/PPPWL58OebNm4fly5cjODgY\nnTp1gpGRUa0xw8PD4eHhoXRt9OjRWLlyJZ4/fw47Ozt4enoiNjYWzs7OsLa2RufOnWFjY4MuXbog\nICDgb3dlNW/eHCkpKdizZw+A6tGSpqYm2rRpg6+++goODg5o1aoVjI2NkZqaCgDo3r37O3F27doF\nf39/7N27F3K5HK1atcK2bdvw4MEDbN68GUC1ReycOXPQunXr/+2PwBAlTGuLwWAwGB8Em9piMBgM\nxgfBGgmDwWAwPgjWSBgMBoPxQbBGwmAwGIwPgjUSBoPBYHwQrJEwGAwG44NgjYTBYDAYH8T/AU8G\n7gUVPGnUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0cbc772940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphical representation of the missing values.\n",
    "x = training_data.columns\n",
    "y = training_data.isnull().sum()\n",
    "sns.set()\n",
    "sns.barplot(x,y)\n",
    "ax = plt.gca()\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x() + p.get_width()/2.,\n",
    "            height + 2,\n",
    "            int(height),\n",
    "            fontsize=12, ha='center', va='bottom')\n",
    "ax.set_xlabel(\"Data Attributes\")\n",
    "ax.set_ylabel(\"count of missing records for each attribute\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seriousdlqin2yrs</th>\n",
       "      <th>revolvingutilizationofunsecuredlines</th>\n",
       "      <th>age</th>\n",
       "      <th>numberoftime3059dayspastduenotworse</th>\n",
       "      <th>debtratio</th>\n",
       "      <th>monthlyincome</th>\n",
       "      <th>numberofopencreditlinesandloans</th>\n",
       "      <th>numberoftimes90dayslate</th>\n",
       "      <th>numberrealestateloansorlines</th>\n",
       "      <th>numberoftime6089dayspastduenotworse</th>\n",
       "      <th>numberofdependents</th>\n",
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       "      <td>0</td>\n",
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       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seriousdlqin2yrs  revolvingutilizationofunsecuredlines  age  \\\n",
       "0                 1                              0.766127   45   \n",
       "1                 0                              0.957151   40   \n",
       "2                 0                              0.658180   38   \n",
       "3                 0                              0.233810   30   \n",
       "4                 0                              0.907239   49   \n",
       "\n",
       "   numberoftime3059dayspastduenotworse  debtratio  monthlyincome  \\\n",
       "0                                    2   0.802982         9120.0   \n",
       "1                                    0   0.121876         2600.0   \n",
       "2                                    1   0.085113         3042.0   \n",
       "3                                    0   0.036050         3300.0   \n",
       "4                                    1   0.024926        63588.0   \n",
       "\n",
       "   numberofopencreditlinesandloans  numberoftimes90dayslate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "3                                5                        0   \n",
       "4                                7                        0   \n",
       "\n",
       "   numberrealestateloansorlines  numberoftime6089dayspastduenotworse  \\\n",
       "0                             6                                    0   \n",
       "1                             0                                    0   \n",
       "2                             0                                    0   \n",
       "3                             0                                    0   \n",
       "4                             1                                    0   \n",
       "\n",
       "   numberofdependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Actual replacement of the missing value using mean value.\n",
    "training_data_mean_replace = training_data.fillna((training_data.mean()))\n",
    "training_data_mean_replace.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seriousdlqin2yrs                        0\n",
       "revolvingutilizationofunsecuredlines    0\n",
       "age                                     0\n",
       "numberoftime3059dayspastduenotworse     0\n",
       "debtratio                               0\n",
       "monthlyincome                           0\n",
       "numberofopencreditlinesandloans         0\n",
       "numberoftimes90dayslate                 0\n",
       "numberrealestateloansorlines            0\n",
       "numberoftime6089dayspastduenotworse     0\n",
       "numberofdependents                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_mean_replace.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>seriousdlqin2yrs</th>\n",
       "      <th>revolvingutilizationofunsecuredlines</th>\n",
       "      <th>age</th>\n",
       "      <th>numberoftime3059dayspastduenotworse</th>\n",
       "      <th>debtratio</th>\n",
       "      <th>monthlyincome</th>\n",
       "      <th>numberofopencreditlinesandloans</th>\n",
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       "      <th>numberrealestateloansorlines</th>\n",
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       "      <th>numberofdependents</th>\n",
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       "      <td>0</td>\n",
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       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
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       "      <td>7</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seriousdlqin2yrs  revolvingutilizationofunsecuredlines  age  \\\n",
       "0                 1                              0.766127   45   \n",
       "1                 0                              0.957151   40   \n",
       "2                 0                              0.658180   38   \n",
       "3                 0                              0.233810   30   \n",
       "4                 0                              0.907239   49   \n",
       "\n",
       "   numberoftime3059dayspastduenotworse  debtratio  monthlyincome  \\\n",
       "0                                    2   0.802982         9120.0   \n",
       "1                                    0   0.121876         2600.0   \n",
       "2                                    1   0.085113         3042.0   \n",
       "3                                    0   0.036050         3300.0   \n",
       "4                                    1   0.024926        63588.0   \n",
       "\n",
       "   numberofopencreditlinesandloans  numberoftimes90dayslate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "3                                5                        0   \n",
       "4                                7                        0   \n",
       "\n",
       "   numberrealestateloansorlines  numberoftime6089dayspastduenotworse  \\\n",
       "0                             6                                    0   \n",
       "1                             0                                    0   \n",
       "2                             0                                    0   \n",
       "3                             0                                    0   \n",
       "4                             1                                    0   \n",
       "\n",
       "   numberofdependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Actual replacement of the missing value using median value.\n",
    "training_data_median_replace = training_data.fillna((training_data.median()))\n",
    "training_data_median_replace.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seriousdlqin2yrs                        0\n",
       "revolvingutilizationofunsecuredlines    0\n",
       "age                                     0\n",
       "numberoftime3059dayspastduenotworse     0\n",
       "debtratio                               0\n",
       "monthlyincome                           0\n",
       "numberofopencreditlinesandloans         0\n",
       "numberoftimes90dayslate                 0\n",
       "numberrealestateloansorlines            0\n",
       "numberoftime6089dayspastduenotworse     0\n",
       "numberofdependents                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_median_replace.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revolvingutilizationofunsecuredlines</th>\n",
       "      <th>age</th>\n",
       "      <th>numberoftime3059dayspastduenotworse</th>\n",
       "      <th>debtratio</th>\n",
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       "  <tbody>\n",
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       "      <th>revolvingutilizationofunsecuredlines</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005898</td>\n",
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       "      <th>age</th>\n",
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       "      <td>0.147705</td>\n",
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       "      <td>-0.215693</td>\n",
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       "    <tr>\n",
       "      <th>numberoftime3059dayspastduenotworse</th>\n",
       "      <td>-0.001314</td>\n",
       "      <td>-0.062995</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006542</td>\n",
       "      <td>-0.008370</td>\n",
       "      <td>-0.055312</td>\n",
       "      <td>0.983603</td>\n",
       "      <td>-0.030565</td>\n",
       "      <td>0.987005</td>\n",
       "      <td>-0.004590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debtratio</th>\n",
       "      <td>0.003961</td>\n",
       "      <td>0.024188</td>\n",
       "      <td>-0.006542</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018006</td>\n",
       "      <td>0.049565</td>\n",
       "      <td>-0.008320</td>\n",
       "      <td>0.120046</td>\n",
       "      <td>-0.007533</td>\n",
       "      <td>-0.044476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthlyincome</th>\n",
       "      <td>0.006513</td>\n",
       "      <td>0.027581</td>\n",
       "      <td>-0.008370</td>\n",
       "      <td>-0.018006</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.086949</td>\n",
       "      <td>-0.010500</td>\n",
       "      <td>0.116273</td>\n",
       "      <td>-0.009252</td>\n",
       "      <td>0.066314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numberofopencreditlinesandloans</th>\n",
       "      <td>-0.011281</td>\n",
       "      <td>0.147705</td>\n",
       "      <td>-0.055312</td>\n",
       "      <td>0.049565</td>\n",
       "      <td>0.086949</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.079984</td>\n",
       "      <td>0.433959</td>\n",
       "      <td>-0.071077</td>\n",
       "      <td>0.074026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numberoftimes90dayslate</th>\n",
       "      <td>-0.001061</td>\n",
       "      <td>-0.061005</td>\n",
       "      <td>0.983603</td>\n",
       "      <td>-0.008320</td>\n",
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       "      <td>-0.079984</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.045205</td>\n",
       "      <td>0.992796</td>\n",
       "      <td>-0.011962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numberrealestateloansorlines</th>\n",
       "      <td>0.006235</td>\n",
       "      <td>0.033150</td>\n",
       "      <td>-0.030565</td>\n",
       "      <td>0.120046</td>\n",
       "      <td>0.116273</td>\n",
       "      <td>0.433959</td>\n",
       "      <td>-0.045205</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.039722</td>\n",
       "      <td>0.129399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numberoftime6089dayspastduenotworse</th>\n",
       "      <td>-0.001048</td>\n",
       "      <td>-0.057159</td>\n",
       "      <td>0.987005</td>\n",
       "      <td>-0.007533</td>\n",
       "      <td>-0.009252</td>\n",
       "      <td>-0.071077</td>\n",
       "      <td>0.992796</td>\n",
       "      <td>-0.039722</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numberofdependents</th>\n",
       "      <td>0.001193</td>\n",
       "      <td>-0.215693</td>\n",
       "      <td>-0.004590</td>\n",
       "      <td>-0.044476</td>\n",
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       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      revolvingutilizationofunsecuredlines  \\\n",
       "revolvingutilizationofunsecuredlines                              1.000000   \n",
       "age                                                              -0.005898   \n",
       "numberoftime3059dayspastduenotworse                              -0.001314   \n",
       "debtratio                                                         0.003961   \n",
       "monthlyincome                                                     0.006513   \n",
       "numberofopencreditlinesandloans                                  -0.011281   \n",
       "numberoftimes90dayslate                                          -0.001061   \n",
       "numberrealestateloansorlines                                      0.006235   \n",
       "numberoftime6089dayspastduenotworse                              -0.001048   \n",
       "numberofdependents                                                0.001193   \n",
       "\n",
       "                                           age  \\\n",
       "revolvingutilizationofunsecuredlines -0.005898   \n",
       "age                                   1.000000   \n",
       "numberoftime3059dayspastduenotworse  -0.062995   \n",
       "debtratio                             0.024188   \n",
       "monthlyincome                         0.027581   \n",
       "numberofopencreditlinesandloans       0.147705   \n",
       "numberoftimes90dayslate              -0.061005   \n",
       "numberrealestateloansorlines          0.033150   \n",
       "numberoftime6089dayspastduenotworse  -0.057159   \n",
       "numberofdependents                   -0.215693   \n",
       "\n",
       "                                      numberoftime3059dayspastduenotworse  \\\n",
       "revolvingutilizationofunsecuredlines                            -0.001314   \n",
       "age                                                             -0.062995   \n",
       "numberoftime3059dayspastduenotworse                              1.000000   \n",
       "debtratio                                                       -0.006542   \n",
       "monthlyincome                                                   -0.008370   \n",
       "numberofopencreditlinesandloans                                 -0.055312   \n",
       "numberoftimes90dayslate                                          0.983603   \n",
       "numberrealestateloansorlines                                    -0.030565   \n",
       "numberoftime6089dayspastduenotworse                              0.987005   \n",
       "numberofdependents                                              -0.004590   \n",
       "\n",
       "                                      debtratio  monthlyincome  \\\n",
       "revolvingutilizationofunsecuredlines   0.003961       0.006513   \n",
       "age                                    0.024188       0.027581   \n",
       "numberoftime3059dayspastduenotworse   -0.006542      -0.008370   \n",
       "debtratio                              1.000000      -0.018006   \n",
       "monthlyincome                         -0.018006       1.000000   \n",
       "numberofopencreditlinesandloans        0.049565       0.086949   \n",
       "numberoftimes90dayslate               -0.008320      -0.010500   \n",
       "numberrealestateloansorlines           0.120046       0.116273   \n",
       "numberoftime6089dayspastduenotworse   -0.007533      -0.009252   \n",
       "numberofdependents                    -0.044476       0.066314   \n",
       "\n",
       "                                      numberofopencreditlinesandloans  \\\n",
       "revolvingutilizationofunsecuredlines                        -0.011281   \n",
       "age                                                          0.147705   \n",
       "numberoftime3059dayspastduenotworse                         -0.055312   \n",
       "debtratio                                                    0.049565   \n",
       "monthlyincome                                                0.086949   \n",
       "numberofopencreditlinesandloans                              1.000000   \n",
       "numberoftimes90dayslate                                     -0.079984   \n",
       "numberrealestateloansorlines                                 0.433959   \n",
       "numberoftime6089dayspastduenotworse                         -0.071077   \n",
       "numberofdependents                                           0.074026   \n",
       "\n",
       "                                      numberoftimes90dayslate  \\\n",
       "revolvingutilizationofunsecuredlines                -0.001061   \n",
       "age                                                 -0.061005   \n",
       "numberoftime3059dayspastduenotworse                  0.983603   \n",
       "debtratio                                           -0.008320   \n",
       "monthlyincome                                       -0.010500   \n",
       "numberofopencreditlinesandloans                     -0.079984   \n",
       "numberoftimes90dayslate                              1.000000   \n",
       "numberrealestateloansorlines                        -0.045205   \n",
       "numberoftime6089dayspastduenotworse                  0.992796   \n",
       "numberofdependents                                  -0.011962   \n",
       "\n",
       "                                      numberrealestateloansorlines  \\\n",
       "revolvingutilizationofunsecuredlines                      0.006235   \n",
       "age                                                       0.033150   \n",
       "numberoftime3059dayspastduenotworse                      -0.030565   \n",
       "debtratio                                                 0.120046   \n",
       "monthlyincome                                             0.116273   \n",
       "numberofopencreditlinesandloans                           0.433959   \n",
       "numberoftimes90dayslate                                  -0.045205   \n",
       "numberrealestateloansorlines                              1.000000   \n",
       "numberoftime6089dayspastduenotworse                      -0.039722   \n",
       "numberofdependents                                        0.129399   \n",
       "\n",
       "                                      numberoftime6089dayspastduenotworse  \\\n",
       "revolvingutilizationofunsecuredlines                            -0.001048   \n",
       "age                                                             -0.057159   \n",
       "numberoftime3059dayspastduenotworse                              0.987005   \n",
       "debtratio                                                       -0.007533   \n",
       "monthlyincome                                                   -0.009252   \n",
       "numberofopencreditlinesandloans                                 -0.071077   \n",
       "numberoftimes90dayslate                                          0.992796   \n",
       "numberrealestateloansorlines                                    -0.039722   \n",
       "numberoftime6089dayspastduenotworse                              1.000000   \n",
       "numberofdependents                                              -0.012678   \n",
       "\n",
       "                                      numberofdependents  \n",
       "revolvingutilizationofunsecuredlines            0.001193  \n",
       "age                                            -0.215693  \n",
       "numberoftime3059dayspastduenotworse            -0.004590  \n",
       "debtratio                                      -0.044476  \n",
       "monthlyincome                                   0.066314  \n",
       "numberofopencreditlinesandloans                 0.074026  \n",
       "numberoftimes90dayslate                        -0.011962  \n",
       "numberrealestateloansorlines                    0.129399  \n",
       "numberoftime6089dayspastduenotworse            -0.012678  \n",
       "numberofdependents                              1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.fillna((training_data.median()), inplace=True)\n",
    "# Get the correlation of the training dataset\n",
    "training_data[training_data.columns[1:]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6dCg1az46ttmvXz/27dsnnSRb0ldffcXXX3+Nh4cH0dHRTJkyRastSnrrrbcoKCjAzc2N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\nIiIi1fk/X7Vhd/q/mPcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0cbc772e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "sns.heatmap(training_data[training_data.columns[1:]].corr(),annot=True,fmt=\".1f\",\n",
    "            cmap=(sns.cubehelix_palette(8, start=.5, rot=-.75)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Percentile based outlier detection\n",
    "def percentile_based_outlier(data, threshold=95):\n",
    "    diff = (100 - threshold) / 2.0\n",
    "    (minval, maxval) = np.percentile(data, [diff, 100 - diff])\n",
    "    return ((data < minval) | (data > maxval))\n",
    "\n",
    "# Another percentile based outlier detection method which is based on inter quertile(IQR) range\n",
    "# import numpy as np\n",
    "# def outliers_iqr(ys):\n",
    "#     quartile_1, quartile_3 = np.percentile(ys, [25, 75])\n",
    "#     iqr = quartile_3 - quartile_1\n",
    "#     lower_bound = quartile_1 - (iqr * 1.5)\n",
    "#     upper_bound = quartile_3 + (iqr * 1.5)\n",
    "#     return np.where((ys > upper_bound) | (ys < lower_bound))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.78278459,  3.65145584,  2.29190495, ...,  0.41774192,\n",
       "        0.70112701,  3.16548016])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mad_based_outlier(points, thresh=3.5):\n",
    "    if len(points.shape) == 1:\n",
    "        points = points[:,None]\n",
    "    median_y = np.median(points, axis=0)\n",
    "    median_absolute_deviation_y = np.median([np.abs(y - median_y) for y in points])\n",
    "    modified_z_scores = [0.6745 * (y - median_y) / median_absolute_deviation_y\n",
    "                         for y in points]\n",
    "\n",
    "    return modified_z_score \n",
    "mad_based_outlier(points=training_data.revolvingutilizationofunsecuredlines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
